2008 SUJIT NAIR ALL RIGHTS RESERVED

©2008

SUJIT NAIR

ALL RIGHTS RESERVED

PHARMACOGENOMIC AND MECHANISTIC

STUDIES ON DIETARY FACTORS

IN CHEMOPREVENTION OF CANCER

by

SUJIT SUKUMAR NAIR

A Dissertation submitted to the

Graduate School-New Brunswick

Rutgers, The State University of New Jersey

in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

Graduate Program in Pharmaceutical Science

written under the direction of

Professor Ah-Ng Tony Kong, Ph.D.,

and approved by

________________________

________________________

________________________

________________________

New Brunswick, New Jersey

May, 2008

ABSTRACT OF THE DISSERTATION

Pharmacogenomic and Mechanistic studies on Dietary Factors

in Chemoprevention of Cancer

by SUJIT SUKUMAR NAIR

Dissertation Director: Professor Ah-Ng Tony Kong

Pharmacogenomic profiling of cancer has recently seen much activity with the

accessibility of the newest generation of high-throughput platforms and technologies. A

myriad of mechanistic studies have been devoted to identifying dietary factors that can

help prevent cancer, with evidence gleaned from epidemiologic studies revealing an

inverse correlation between the intake of cruciferous vegetables and the risk of certain

types of cancer. To develop a comprehensive understanding of cancer pathogenesis, and

potential for chemopreventive intervention with dietary factors, an integrated approach

that encompasses both pharmacogenomic and mechanistic aspects is desirable. Our

transcriptomic profiling of butylated hydroxyanisole-induced Nuclear Factor-E2-related

factor 2 (Nrf2)-dependent genes in Nrf2-deficient mice identified several germane

molecular targets for prevention. Toxicogenomic analyses of endoplasmic reticulum

stress inducer tunicamycin in Nrf2-deficient mice elucidated Nrf2-regulated unfolded

protein response effects. Mechanistic studies on a combination of sulforaphane and (-)

ii

epigallocatechin-3-gallate in HT-29 AP-1 (Activator Protein-1) cells revealed a synergy

in colon cancer chemoprevention. Pharmacogenomic studies of this combination in PC-3

AP-1 cells provided a discursive framework for understanding putative crosstalk between

Nrf2 and AP-1 in prostate cancer chemoprevention. Regulatory potential for concerted

modulation of Nrf2 and Nuclear Factor-κB (Nfκb1) in inflammation and carcinogenesis

was delineated by bioinformatic analyses. Metabolomic approaches identified potential

prognostic biomarkers in human prostate cancer. Differential biological networks in

prostate cancer were elicited in androgen-dependent 22Rv1 cells, androgen- and

estrogen-dependent LNCaP cells and androgen-independent DU 145 and PC-3 cells.

Taken together, our identification of Nrf2-regulated molecular targets by expression

profiling using dietary factors, synergistic effects in combinatorial use of dietary factors

in colon cancer, regulatory studies on crosstalk between Nrf2 and AP-1 in prostate

cancer, bioinformatic analyses of concerted modulation of Nrf2 and Nfkb1 in

inflammation and carcinogenesis, metabolomic identification of biomarkers, and

delineation of target hubs in differential prostate cancer biological networks, greatly

enhance our understanding of the transcriptional circuitry in cancer and important master

regulatory nodes including Nrf2 that might potentially be exploited for chemopreventive

intervention with dietary factors.

iii

PREFACE

This dissertation is submitted for the Degree of Doctor of Philosophy in

Pharmaceutical Science at Rutgers, The State University of New Jersey. It serves as

documentation of my research work carried out between August 2002 and April 2008

under the supervision of Dr. Ah-Ng Tony Kong at the Department of Pharmaceutics,

Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey. To the

best of my knowledge, this work is original, except where suitable references are made to

previous work.

The dissertation consists of eight chapters. Chapter 1 reviews anti-cancer

chemopreventive compounds and provides pharmacogenomic and mechanistic insights

into the etiopathogenesis of cancer and the potential for chemopreventive intervention.

The following seven chapters consist of manuscripts that are already accepted by or are

intended to be submitted to peer-reviewed journals. Chapter 2 investigates the

pharmacogenomics and the spatial regulation of global gene expression profiles elicited

by cancer chemopreventive agent butylated hydroxyanisole (BHA) in Nrf2-deficient

mice. Chapter 3 elucidates the toxicogenomics and the spatial regulation of global gene

expression profiles elicited by endoplasmic reticulum stress inducer tunicamycin in Nrf2-

deficient mice. Chapter 4 investigates the synergistic effects of a combination of dietary

factors sulforaphane and (-) epigallocatechin-3-gallate (EGCG) in HT-29 AP-1 human

colon carcinoma cells. Chapter 5 delineates the regulation of gene expression by a

combination of dietary factors sulforaphane and EGCG in PC-3 AP-1 human prostate

adenocarcinoma cells and Nrf2-deficient murine prostate. Chapter 6 elucidates the

iv

regulatory potential for concerted modulation of Nrf2- and Nfkb1-mediated gene

expression in inflammation and carcinogenesis. Chapter 7 describes the

pharmacogenomic investigation of potential prognostic biomarkers in human prostate

cancer. Chapter 8 elucidates differential regulatory networks elicited in androgen-

dependent, androgen- and estrogen-dependent, and androgen-independent human prostate

cancer cell lines using a systems biology approach. Finally, the Appendix to the

dissertation consists of Tables of results from all eight chapters that have been referred to

when applicable in the main body of the text comprising these chapters. Together, these

findings provide extensive pharmacogenomic and mechanistic evidence that underscore

the important utility of dietary factors, alone or in combination, as rational and

efficacious strategies in the chemoprevention of cancer.

Sujit Nair

April 2008

v

ACKNOWLEDGEMENT

I write these words with a profound sense of gratitude towards my beloved Guru and

Mother - Śrī Mātā Amritānandamayi Dēvi – known universally as ‘The Hugging Saint’,

or simply ‘Ammā’. It was Her Love and Her Compassion that helped me persevere in this

doctoral program. I know for sure that but for Her Grace, and Her constant guidance at

every step of the way over these past six years, this dissertation would never have seen

the light of day. Indeed, it would be imprudent for me to attempt to describe Her whom I

do not comprehend myself. I can only dedicate this dissertation in all humility at Her

Lotus Feet and pray for Her Grace.

I thank my Advisor Dr. Ah-Ng Tony Kong, for all that I have learnt from him in the past

six years in the Department of Pharmaceutics at the Ernest Mario School of Pharmacy.

Without him as my Advisor, many of the wonderful lessons that came my way would

simply not have been, and I deeply thank him for playing his part as an instrument of

Divine Will to impeccable perfection. I also acknowledge the members of my Committee

- Dr. Tamara Minko and Dr. Guofeng You from the Department of Pharmaceutics, and

Dr. Li Cai from the Biomedical Engineering program. My deepest thanks are due to Dr.

Tamara Minko for her tremendous support and help at all times. I am extremely grateful

to this wonderfully kind person who stood by me in hard times so that I could

successfully graduate with my doctoral degree, and left no stone unturned in helping

people whenever she could - I do wish we could have more faculty who would be more

like her. I am also grateful to Dr. Li Cai for his patient training on the various

vi

bioinformatic approaches during our highly-productive collaboration with four

manuscripts (Chapters 5 through 8) standing as mute testimony to the fruits of our hard

work.

Many thanks are due to Dean Barbara Bender, Ph.D., Associate Dean of the Graduate

School-New Brunswick for all her support over the past three years, initially during the

Head-TA (Teaching Assistant) program and later when I served as Project Facilitator for

New International TAs at two consecutive Annual TA Orientations, and not least of all

for her tremendous help and support in ensuring the successful completion of this

doctoral program. I remain extremely grateful to this wonderfully exuberant and kind

person for all her efforts to help me at all times. I also thank Jay Rimmer and Barbara

Sirman from the Graduate School-New Brunswick for their willingness to help at all

times.

I thank Dean John L. Colaizzi, Ph.D., R.Ph., for his terrific training while I was his TA in

the Department of Pharmacy Practice and Administration for his courses in Introduction

to Pharmaceutical Care, Pharmacy Convocations, and History of Pharmacy. He is an

eloquent speaker who puts his heart into his lectures that he delivers in the most

impeccable manner, and I am deeply inspired by his teaching style and his degree of

perfection. I also thank Dean Christopher Molloy, Ph.D., R.Ph., for his help and kindness

in ensuring the successful completion of this doctoral program. I am extremely grateful to

Ms. Margaret Snyder (Margie) in the Dean’s Office for her tremendous help and support

to me in the past six years, even past her retirement from the Ernest Mario School of

vii

Pharmacy. She was always there for me, whether for tuition remission cards, health

insurance questions, TA/GA (Teaching Assistant/Graduate Assistant) funding issues, and

right through my successful Defense of this dissertation. She is a wonderful person and I

thank her wholeheartedly for her help at all times. I also thank Ms. Jenny Visaggio,

Business Manager of the Ernest Mario School of Pharmacy, for continuing to make me

feel welcome in the Dean’s Office after Margie’s retirement and for all her gracious

support and help. Thanks are also due to Ms. Lisa Mulé and Ms. Rosemary Hayes in the

Dean’s Office for their help at all times. I also acknowledge the terrific help of Ms.

Marianne Shen, Ms. Sharana Taylor and Ms. Amy Grabowski throughout my stay here.

I would like to acknowledge the various faculty members whom I worked with as Head-

TA in the Department of Pharmaceutics, some of whom I have already made mention of

earlier. These included Dr. Tamara Minko (Drug Delivery II), Dr. Guofeng You

(Introduction to Pharmaceutics and Laboratory), Dr. Thomas Cook (Drug Delivery I and

Laboratory) and Dr. Ah-Ng Tony Kong (Introduction to Biopharmaceutics and

Pharmacokinetics). I also thank Dean Nancy-Cintron Budét for her kindness and help

with arrangements for special-needs students first during my tenure as TA with Dean

Colaizzi and later as Head-TA in the Department of Pharmaceutics.

This acknowledgement would be sorely incomplete without mentioning my beloved

students of the Pharm.D. program at the Ernest Mario School of Pharmacy. I have a deep

sense of gratitude as I thank the almost 1200 students of the Pharm. D. classes of 2006,

2008, 2009 and 2010, with an average class strength of approximately 300 students, for

whom I served as TA and Head-TA during two years between Fall 2004 and Spring

viii

2006. I especially thank the Pharm.D. class of 2008 since these fantastic students stayed

with me for four consecutive semesters, and I was able to strike a personal chord with

every one of these wonderful students on a first-name basis. I bow down before the

intelligence, enthusiasm and joie de vivre of these terrific students from all these classes

who were all so friendly to me, and I wish them all great success in their future pursuits.

It would be impossible to do research work without adequate funding for biological and

chemical necessities, and I am grateful to the National Institutes of Health (NIH) for their

grants NIH RO1CA094828 (Nrf2 grant), NIH RO1CA073674 (Colon cancer and

isothiocyanates grant) and NIH RO1CA118947 (Prostate cancer grant) all awarded to Dr.

Ah-Ng Tony Kong. I am also grateful to these grants for facilitating my funding as a

Graduate Assistant (GA) for four years, and to the Department of Pharmaceutics for

funding me as a Teaching Assistant (TA) for two years at the Ernest Mario School of

Pharmacy.

It is with great pleasure that I acknowledge the unique fraternity of the members of my

laboratory – Room 013 – at the Ernest Mario School of Pharmacy. Firstly, I would like to

make special mention of Dr. Vidya Hebbar for all her help and friendship over the last six

years, for training me on various aspects of laboratory techniques and for all the help in

the initial years and later, even after she left this laboratory. I also thank Avanthika

Gopalakrishnan-Barve, Wen Lin, Celine Liew, Young-Sam Keum, Jung-Hwan Kim,

Rong Hu, Changjiang Xu, Wenge Li, Chi Chen, Guoxiang Shen, Bok-Ryang Kim, Woo-

Sik Jeong, In-Wha Kim, Tin-Oo Khor, Siwang Yu, Xiaoling Yuan, Sherif Ibrahim, Yury

ix

Gomez, Hang Guo, Usha Yerramilli, William Ka Lung Cheung, Jin-Liern Hong, Rachel

Wu, Tien-Yuan Wu and Constance Lay-Lay Saw. Without this eclectic mix of talent and

expertise, life would not have been so interesting at Rutgers, The State University of New

Jersey, and I am very grateful for the wonderful sense of fraternity that we shared

together. I am also very grateful to Mrs. Hui Pung for handling all our Purchase Order

(PO) requests very promptly, for all her help and understanding over the past six years,

and for bringing in her own unique flavor of maternal kindness to our wonderful group.

Indeed, graduate student life, embellished as it is with the rigors of academic

requirements, financial considerations, and a certain degree of acquiescing compromise,

could never be complete without a group of friends both at home and outside the

workplace that make it so much easier to breathe. It is with gratitude that I thank the

Sharma family, Volkan Demirbas, Srinivas Maloor, Siddharth Madan, Manjul Apratim,

Vishal Jain, Aakash Jain and Vatsal Shah for all the help they have provided me.

I thank my parents, Indira and Sukumar, who kindled the lamp of my life and took great

pains in furthering the cause of my academic pursuits. Sunita, my sister, as well as

Jayaprakash, Aditya and Aiswarya also deserve thanks for their tremendous help and

support. I also thank my uncle, Prabhakaran, who has been a great source of inspiration

and support over all these years. Finally, I will gladly seize this opportunity to express

my deepest thanks to some of my most wonderful brothers and sisters, all beloved

children of Ammā, without whose help I could not have survived alone in the United

States during all these trying and testing years. I also know that mere words would not

x

suffice to express the depth of emotion that binds us all together in Ammā’s Pure Love. I

am indebted to Pushpa Nayar for her terrific help at all times, whether it was providing

me with food, or attending to the various needs of my dissertation, and offering words of

supportive encouragement whenever adversity struck me. I am also indebted to Damu

and Radhika Menon for the perspicacious words of advice and strong support at every

hour of crisis that befell me. I am very grateful to Roopa, Shrirang and Ayush for their

great support on so many various occasions. I also thank Seetha, Ramesh, Sashu and

Shivesh for making me so welcome in their home at all times, and for all the terrific

support in completing this doctoral program. Further, I thank Meera and Venkatesh for

their tremendous encouragement and concern for me over the years. I also thank

Shabarinath, Sai Achuthan, Deepak and Shimona Nambiar for their help and

encouragement during the course of my program. I am extremely indebted to my dear

friends Pavithra and Vipin for their inimitable support, encouragement and help at very

crucial and grief-stricken hours of this doctoral program. I do also thank Dr. Geetha

Kumar for her support, understanding and compassion in empathizing with me at all

times. Besides, my most beloved friends – Sankaran, Kaushal and Kartik, Ajay, Arun and

Raji, Ranjith, and Sahil – outshone themselves in their ability to empathize with me and

provide me much-needed succour in hours of grief. I also thank Swāmi

Rāmakrishnānanda Puri, Swāmi Amritātmananda Puri, Swāmi Pranavāmritānanda Puri,

Swāmi Amritaswaroopānanda Puri, Swāmi Poornāmritānanda Puri, Swāmi Dayāmritā

Chaitanyā and Swāmi Shubāmritā Chaitanyā for providing me inspiration, guidance and

love in my difficult times. I simply bow down before all the above-mentioned children of

Ammā who beautifully expressed Ammā’s Love and Compassion in repeatedly bailing

xi

me out of difficult situations. But for this wonderful social network created by Ammā’s

Divine Grace, this dissertation would not have been possible today. I also thank all my

beloved brothers and sisters of the New York and New Jersey Satsangs who have

extended a helping hand to me on so many occasions during the past six years.

In the last analysis, it is Ammā’s Grace, Ammā’s Love, Ammā’s Compassion, and

Ammā’s Will that prevailed over all else. This dissertation is but a minuscule fragment in

the plethora of divine events that stand wondrous testimony to Her Infinite Power and

Grace. Yet, I am overjoyed and grateful that the Divine Mother granted me a once-in-a-

lifetime opportunity to dedicate this dissertation, howsoever insignificant, at Her Lotus

Feet. May our love for Ammā blossom like the scintillating radiance of the rising sun,

and may Ammā’s Divine Grace, Love and Light flood all our lives with eternal peace.

xii

DEDICATION

To my beloved Guru and Mother,

Śrī Mātā Amritānandamayi Dēvi (Ammā)

© M.A. Center

xiii

TABLE OF CONTENTS

Abstract of the Dissertation ……………………………………………………… ii

Preface …………………………………………………………………………….. iv

Acknowledgement…………………………………………………………………. vi

Dedication …………………………………………………………………………. xiii

Table of Contents …………………………………………………………………. xiv

List of Tables in Appendix………………………………………………………... xxii

List of Figures ……………………………………………………………………... xxvi

CHAPTER 1

Natural Dietary Anti-cancer chemopreventive compounds:

Redox-mediated Differential Signaling Mechanisms in

Cytoprotection of Normal Cells Versus Cytotoxicity in Tumor

Cells

1.1. Abstract 1

1.2. Introduction 3

1.3. Natural dietary anti-cancer chemopreventive compounds 6

1.4. Redox-mediated signaling 9

1.4.1. Oxidative stress and redox circuitry 9

1.4.2. Redox-sensitive transcription factors 11

1.4.3. The Keap1-Nrf2 axis in redox signaling 13

xiv

1.5. Gene expression and in vivo pharmacological effects 16

1.5.1. The Nrf2 paradigm in gene expression 16

1.5.2. Transcriptome profiling of putative Nrf2 coactivators and

corepressors 17

1.5.3. Coordinated regulation of Phase I, II and III drug metabolizing

enzyme/transporter genes via Nrf2 19

1.5.4. Spatial and temporal control of Nrf2-mediated gene expression 20

1.5.5. Pharmacotoxicogenomic relevance of redox-sensitive Nrf2 21

1.5.6. Redox regulation of cellular signaling molecules leading to cell

death mechanisms 23

1.5.7. Modulation of apoptosis and cell-cycle control genes via Nrf2 26

1.6. Integrated systems biology approach to cancer

chemoprevention 26

1.7. Concluding Remarks 27

1.8. Acknowledgements 28

CHAPTER 2 Pharmacogenomics of Phenolic Antioxidant Butylated

hydroxyanisole (BHA) in the Small Intestine and Liver of Nrf2

Knockout and C57BL/6J Mice

2.1. Abstract 33

2.2. Introduction 34

2.3. Materials and Methods 37

xv

2.4. Results 42

2.4.1. BHA-Modulated Gene Expression Patterns in Mouse Small

Intestine and Liver 42

2.4.2. Quantitative Real–Time PCR Validation of Microarray Data 42

2.4.3. BHA-Induced Nrf2-Dependent Genes in Small Intestine and Liver 43

2.4.4. BHA-Suppressed Nrf2-Dependent Genes in Small Intestine and

Liver 46

2.5. Discussion 47


CHAPTER 3 Toxicogenomics of Endoplasmic Reticulum stress inducer

Tunicamycin in the Small Intestine and Liver of Nrf2

Knockout and C57BL/6J Mice

3.1. Abstract 60



3.4. Results 68

3.4.1. TM-Modulated Gene Expression Patterns in Mouse Small

Intestine and Liver 68

3.4.2. Quantitative Real–Time PCR Validation of Microarray Data 69

3.4.3. TM-Induced Nrf2-Dependent Genes in Small Intestine and Liver 69

xvi

3.4.4. TM-Suppressed Nrf2-Dependent Genes in Small Intestine and

Liver 71

3.5. Discussion 72


CHAPTER 4 Synergistic effects of a combination of dietary factors

sulforaphane and (-) epigallocatechin-3-gallate in HT-29 AP-1

human colon carcinoma cells

4.1. Abstract 88



4.4. Results 100

4.4.1. Transactivation of AP-1 luciferase reporter by combinations of

SFN and EGCG 100

4.4.2. SOD attenuates the synergism elicited by combinations of SFN

and EGCG 101

4.4.3. Isobologram analyses and combination indices for the

combinations of SFN and EGCG 102

4.4.4. Viability of the HT-29 AP-1 cells with the combinations of SFN

and EGCG 102

4.4.5. Inhibition of EGCG-induced senescence by the combinations of

SFN and EGCG 103

xvii

4.4.6. Temporal gene expression profiles elicited by combinations of

SFN and EGCG and attenuation by SOD 104

4.4.7. Protein expression with the combinations of SFN and EGCG 106

4.4.8. HDAC inhibitor Trichostatin A potentiates the synergism elicited

by the low-dose combination of SFN and EGCG 106

4.4.9. Cytoplasmic and Nuclear HDAC Activity Assays for the


4.4.10. Reactive oxygen species and SOD may modulate AP-1

transactivation by the combinations of SFN and EGCG 108

4.5. Discussion 108


CHAPTER 5 Regulation of gene expression by a combination of dietary

factors sulforaphane and (-) epigallocatechin-3-gallate in PC-

3 AP-1 human prostate adenocarcinoma cells and Nrf2-

deficient murine prostate

5.1. Abstract 124



5.4. Results 138

5.4.1. Diminished transactivation of AP-1 luciferase reporter by


xviii

5.4.2. Viability of the PC-3 AP-1 cells with the combinations of SFN

and EGCG 139

5.4.3. Temporal gene expression profiles elicited by combinations of

SFN and EGCG 139

5.4.4. Comparative promoter analyses of NRF2 and AP-1, as well as

ATF-2 and ELK-1, for conserved Transcription Factor Binding

Sites (TFBS) 141

5.4.5. Temporal microarray analyses of genes modulated by

SFN+EGCG combination in the prostate of NRF2-deficient mice 142

5.5. Discussion 143


CHAPTER 6

Regulatory potential for concerted modulation of Nrf2- and

Nfkb1-mediated gene expression in inflammation and

carcinogenesis

6.1. Abstract 157



6.4. Results 165

6.4.1. Identification of microarray datasets bearing inflammation/injury

or cancer signatures 165

xix

6.4.2. Comparative promoter analyses of Nrf2(Nfe2l2) and Nfkb1 for

conserved Transcription Factor Binding Sites (TFBS) 166

6.4.3. Multiple species alignment of Nrf2 (Nfe2l2) and Nfkb1 sequences 167

6.4.4. Construction and validation of canonical first-generation

regulatory network involving Nrf2(Nfe2l2) and Nfkb1 168

6.4.5. Putative model for Nrf2-Nfkb1 interactions in inflammation and

carcinogenesis 169

6.5. Discussion 170


CHAPTER 7

Pharmacogenomic investigation of potential prognostic

biomarkers in human prostate cancer

7.1. Abstract 192



7.4. Results 199

7.4.1. Genes modulated in human prostate cancer 199

7.4.2. Biomarker filtration 199

7.4.3. Metabolomics analyses 200

7.4.4. Identification of TFBS-association signatures 201

7.5. Discussion 202


xx

CHAPTER 8

Differential regulatory networks elicited in androgen-

dependent, androgen- and estrogen-dependent, and

androgen-independent human prostate cancer cell lines

8.1. Abstract 215



8.4. Results 222

8.4.1. Microarray analyses for cancerous and non-cancerous prostate

cell lines 222

8.4.2. Biological Networks in different prostate cancer cell lines 223

8.4.3. Metabolomics analyses 224

8.4.4. Regulatory element analyses 225

8.5. Discussion 225


APPENDIX 253

REFERENCES 311

CURRICULUM VITA 332

xxi

LIST OF TABLES IN APPENDIX

Table 1.1. Major Phase II detoxifying/antioxidant genes

upregulated by select dietary chemopreventive agents

and downregulated by a toxicant via the redox-

sensitive transcription factor Nrf2

254

Table 2.1. Oligonucleotide primers used in quantitative real-time

PCR (qRT-PCR) 255

Table 2.2. BHA-induced Nrf2-dependent genes in mouse small

intestine and liver

256-261

Table 2.3. BHA-suppressed Nrf2-dependent genes in mouse small

intestine and liver

262-265

Table 3.1. Oligonucleotide primers used in quantitative real-time

PCR (qRT-PCR)

266

Table 3.2. TM-induced Nrf2-dependent genes in mouse small

intestine and liver

267-271

xxii

Table 3.3. TM-suppressed Nrf2-dependent genes in mouse small

intestine and liver

272-275

Table 4.1. Oligonucleotide primers used for quantitative real-

time PCR (qRT-PCR)

276

Table 4.2. Temporal gene expression profiles elicited by

combinations of SFN and EGCG

277-278

Table 4.3. Temporal gene expression profiles elicited by

combinations of SFN and EGCG in the presence of

SOD

279-280

Table 5.1.A. Human and Murine Matrix Families conserved

between Nrf2 and AP-1

281

Table 5.1.B. Human and Murine Matrix Families conserved

between ATF-2 and ELK1

282

Table 5.2. Matrices with conserved regulatory sequences in

promoter regions of human, or murine, NRF2 and

AP-1

283-289

xxiii

Table 5.3. Temporal microarray analyses of genes suppressed

by SFN+EGCG combination in the prostate of NRF2-

deficient mice

290-293

Table 6.1. Microarray datasets bearing inflammation/injury or

cancer signatures

294-295

Table 6.2. Human and Murine Matrix Families conserved

between Nrf2 and Nfkb1

296

Table 6.3.A. Multiple species alignment for Nfe2l2 297

Table 6.3.B. Multiple species alignment for Nfkb1 298

Table 6.4. Canonical first-generation regulatory network

members representing putative crosstalk between

Nrf2(Nfe2l2) and Nfkb1 in inflammation-associated

carcinogenesis

299

Table 7.1. Major gene clusters modulated in human prostate

cancer

300-302

xxiv

Table 8.1. Human prostate cell lines used in this study and their

descriptors

303

Table 8.2. Genes modulated in all prostate cancer cell lines 304-310

xxv

LIST OF FIGURES

Figure 1.1. The Keap1-Nrf2 axis in redox signaling – Proposed model

for Nrf2 redox signaling 29-30

Figure 1.2. The Nrf2 paradigm in gene expression - Proposed model

for a multimolecular coactivator-corepressor complex that

elicits transcriptional events through the ARE 31-32

Figure 2.1. Chemical Structure of Butylated hydroxyanisole (BHA) 56

Figure 2.2. Schematic representation of experimental design 57

Figure 2.3. Regulation of Nrf2-dependent gene expression by BHA in

mouse small intestine and liver 58

Figure 2.4. Correlation of microarray data with quantitative real-

time PCR data 59

Figure 3.1. Chemical Structure of Tunicamycin (TM) 84

Figure 3.2. Schematic representation of experimental design 85

xxvi

Figure 3.3. Regulation of Nrf2-dependent gene expression by TM in

mouse small intestine and liver 86

Figure 3.4. Correlation of microarray data with quantitative real-

time PCR data 87

Figure 4.1. Transactivation of AP-1 luciferase reporter by

combinations of SFN and EGCG, and attenuation by SOD 117

Figure 4.2. Isobologram analyses of synergy between combinations of

SFN and EGCG 118

Figure 4.3. Viability of the HT-29 AP-1 cells with the combinations of

SFN and EGCG 119

Figure 4.4. Inhibition of EGCG-induced senescence by the


Figure 4.5. Protein expression with the combinations of SFN and

EGCG 121

xxvii

Figure 4.6. HDAC inhibitor Trichostatin A potentiates the synergism

elicited by the low-dose combination of SFN and EGCG 122

Figure 4.7. Reactive oxygen species and SOD may modulate AP-1

transactivation by the combinations of SFN and EGCG 123

Figure 5.1A. Diminished transactivation of AP-1 luciferase reporter by


Figure 5.1B. Viability of the PC-3 AP-1 cells with the combinations of

SFN and EGCG 148

Figure 5.2. Temporal gene expression profiles elicited by

combinations of SFN and EGCG 149-153

Figure 5.3. Conserved Transcription Factor Binding Sites (TFBS) in

promoter regions of ATF-2 and ELK-1 154-156

Figure 6.1. Conserved TFBS between murine Nfe2l2 and Nfkb1 (A)

and human NFE2l2 and NFKB1 (B) 177-179

xxviii

Figure 6.2. Multiple species alignment for Nfe2l2 (A) and Nfkb1 (B)

with phylogenetic tree (C) and conserved TFBS among

top matching human sequences (D) 180-184

Figure 6.3. Canonical regulatory network (A) for Nrf2-Nfkb1

interactions in inflammation-associated carcinogenesis

and literature networks in human (B) or mouse (C) and

functional crosstalk (D) 185-189

Figure 6.4. Putative model for Nrf2-Nfkb1 interactions in

inflammation and carcinogenesis 190-191

Figure 7.1. Major gene clusters modulated in human prostate cancer 207

Figure 7.2. Candidate biomarkers in human prostate cancer (A) and

putative crosstalk between them (B) 208-209

Figure 7.3. Functional biological networks in human prostate cancer

including (A) Cell Death, Cellular Growth and

Differentiation, Cellular Development Network, (B) Gene

Expression, Cancer, Cellular Growth and Proliferation

Network, and (C) Merged Network 210-213

xxix

Figure 7.4. Identification of TFBS-association signatures 214

Figure 8.1. Clustering of human prostate cell lines used in the study 231

Figure 8.2. Biological network (A) in androgen-dependent human

prostate cancer 22Rv1 cells and sub-network structure in

upper (B) and lower (C) panels 232-235

Figure 8.3. Biological network (A) in androgen- and estrogen-

dependent human prostate cancer LNCaP cells and sub-

network structure (B) 236-238

Figure 8.4. Biological network (A) in androgen-dependent human

prostate cancer MDA PCa 2b cells and sub-network

structure (B) 239-241

Figure 8.5. Biological network in androgen-independent human

prostate cancer DU 145 cells 242

Figure 8.6. Biological network in androgen-independent human

prostate cancer PC-3 cells 243

xxx

xxxi

Figure 8.7. Cell Death, Cancer, Cellular Development Network 244

Figure 8.8. Cancer, Endocrine System Development and Function,

Organ Development Network 245

Figure 8.9. Cancer, Cell Death, Cellular Movement Network 246

Figure 8.10. Gene Expression, Cell Cycle, Cancer Network 247

Figure 8.11. Comprehensive overview (A) of all Metabolomics

Networks in prostate cancer cell lines and sub-network

structure in upper (B) and lower (C) panels 248-251

Figure 8.12. Regulatory element analyses for identification of TFBS-

association signatures 252

1

CHAPTER 1

INTRODUCTION

Natural Dietary Anti-cancer chemopreventive compounds: Redox-mediated

Differential Signaling Mechanisms in Cytoprotection of Normal Cells Versus

Cytotoxicity in Tumor Cells 1,2,3

1.1. Abstract

Pharmacogenomic profiling of cancer has recently seen much activity with the

accessibility of the newest generation of high-throughput platforms and technologies.

To develop a comprehensive understanding of cancer pathogenesis, and potential for

chemopreventive intervention with dietary factors, an integrated approach that

encompasses both pharmacogenomic and mechanistic aspects is desirable. Many dietary

phytochemicals exhibit health beneficial effects including prevention of diseases such

as cancer, neurological, cardiovascular, inflammatory and metabolic. Evolutionarily,

herbivorous and omnivorous animals have been ingesting plants. This interaction

between “animal-plant” ecosystems has resulted in an elaborate system of detoxification

1Part of this chapter has been published as a review article – Nair, S., Kong, A.-N. T., Acta Pharmacol Sin., 2007 Apr;28(4):459-72. 2Keywords : Redox, Nrf2, Keap1, NES, NLS, gene expression profiles 3Abbreviations : ROS, Reactive oxygen species; RNS, Reactive Nitrogen species; Nrf2, Nuclear Factor-E2-related factor 2; ARE, Antioxidant response element; Keap1, Kelch-like erythroid CNC homologue (ECH)-associated protein 1; BHA, Butylated hydroxyanisole; EGCG, epigallocatechin-3-gallate; SFN, Sulforaphane; ER, Endoplasmic reticulum; UPR, Unfolded protein response; NES, Nuclear export signal; NLS, Nuclear localization signal; CBP, CREB-binding protein; Ncor1, nuclear receptor co-repressor 1; Nrip1, nuclear receptor co-repressor interacting protein; Ncoa3, nuclear receptor co-activator 3; Ncoa5, nuclear receptor co-activator 5; P/CAF, P300/CBP-associated factor; MAPK, mitogen activated protein kinase.

2

and defense mechanisms evolved by animals including humans. Mammalian, including

human, cells respond to these dietary phytochemicals by “non-classical receptor

sensing” mechanism of electrophilic chemical-stress typified by ‘thiol modulated”

cellular signaling events primarily leading to gene expression of pharmacologically

beneficial effects, but sometimes unwanted cytotoxicity. Our laboratory has been

studying two groups of dietary phytochemical cancer chemopreventive compounds

(isothiocyanates and polyphenols), which are effective in chemical-induced as well as

genetically induced animal carcinogenesis models. These compounds typically generate

“cellular stress” and modulate gene expression of Phase II detoxifying/antioxidant

enzymes. Indeed, electrophiles, reactive oxygen species (ROS) and reactive nitrogen

species (RNS) are known to act as second messengers in the modulation of many

cellular signaling pathways leading to gene expression changes and pharmacological

responses. Redox-sensitive transcription factors such as Nrf2, AP-1, NF-κB, to cite a

few examples, sense and transduce changes in cellular redox status and modulate gene

expression responses to oxidative and electrophilic stresses presumably via sulfhyrdryl

modification of critical cysteine residues found on these proteins and/or other upstream

redox-sensitive molecular targets. In the current review, we will explore dietary cancer

chemopreventive phytochemicals, discuss the link between oxidative/electrophilic

stresses and the redox circuitry and consider different redox-sensitive transcription

factors. We will also discuss the Keap1-Nrf2 axis in redox signaling of induction of

Phase II detoxifying/antioxidant defense mechanisms, an important target and

preventive strategy for normal cells against carcinogenesis, and conversely inhibition of

cell growth/inflammatory signaling pathways that would confer therapeutic intervention

3

in many types of cancers. Further, we will summarize the Nrf2 paradigm in gene

expression, the pharmacotoxicogenomic relevance of redox-sensitive Nrf2, and the

redox regulation of cell death mechanisms. Finally, we will discuss the relevance of an

integrated systems biology approach to cancer chemoprevention.

1.2. Introduction

Many dietary phytochemicals exhibit beneficial effects to health including prevention of

diseases such as cancer, as well as neurological, cardiovascular, inflammatory and

metabolic diseases. Evolutionarily, herbivorous and omnivorous animals have been

ingesting plants. This interaction between “animal-plant” ecosystems has resulted in an

elaborate system of detoxification and defense mechanisms evolved by animals

including humans. Indeed, the condition of stress generated by electrophiles or

xenobiotics may be referred to as electrophilic stress. Mammalian, including human,

cells respond to these dietary phytochemicals by “non-classical receptor sensing”

mechanism of electrophilic chemical-stress typified by ‘thiol modulated” cellular

signaling events primarily leading to gene expression of pharmacologically beneficial

effects, but sometimes unwanted cytotoxicity also. Our laboratory has been studying

two groups of dietary phytochemical cancer chemopreventive compounds

(isothiocyanates and polyphenols) (reviewed in references[1,2]), which are effective in

chemical-induced as well as genetically-induced animal carcinogenesis models[3,4].

These compounds typically generate “cellular stress” and modulate gene expression of

Phase II detoxifying/antioxidant enzymes.

4

Multicellullar organisms rely on highly organized pathways to orchestrate the many

extracellular clues received by the cells and to convert them into specific physiological

processes. The classical first step in this cascade of molecular events that are

collectively referred to as signal transduction pathways is the specific interaction of an

extracellular ligand with its receptor at the cell membrane[5]. Indeed, reactive oxygen

species (ROS) and reactive nitrogen species (RNS) have been proposed as second

messengers in the activation of several signaling pathways leading to mitogenesis or

apoptosis[5]. Effective transmission of information requires specificity, and how ROS

signaling occurs with specificity and without oxidative damage remains poorly

understood[6]. In addition, gene expression responses to oxidative stress are necessary

to ensure cell survival and are largely attributed to specific redox-sensitive transcription

factors[7]. Redox-sensitive cysteine residues are known to sense and transduce changes

in cellular redox status caused by the generation of ROS and the presence of oxidized

thiols[8].

Sporn and Liby[9] noted that the principal need in the chemoprevention of cancer

remains the discovery of new agents that are effective and safe, and the development of

new dose-scheduling paradigms that will allow their beneficial use over relatively long

periods of time (but not necessarily constantly) in a manner that is essentially free of

undesirable side effects. It has been reported[10] that human cancers overexpress genes

that are specific to a variety of normal human tissues, including normal tissues other

than those from which the cancer originated suggesting that this general property of

cancer cells plays a major role in determining the behavior of the cancers, including

their metastatic potential. Surh[11] observed that disruption or deregulation of

5

intracellular-signalling cascades often leads to malignant transformation of cells, and it

is therefore important to identify the molecules in the signalling network that can be

affected by individual chemopreventive phytochemicals to allow for better assessment

of their underlying molecular mechanisms. Conney[12] suggested that tailoring the

chemopreventive regimen to the individual or to groups of individuals living under

different environmental conditions, or with different mechanisms of carcinogenesis,

may be an important aspect of cancer chemoprevention in human populations. Given

the inability to guarantee the long-term safety of current chemopreventive regimens,

Sporn[9] suggested two approaches- the more widespread use of low-dose combinations

of chemopreventive agents, with the goal of achieving a therapeutic synergy between

individual drugs while reducing their individual toxicities; or the use of intermittent,

rather than constant, chronic dosing of chemopreventive agents.

In the current review, we will attempt to shed light on redox-mediated signaling

translating into gene expression and in vivo pharmacological events. We will explore

dietary cancer chemopreventive phytochemicals, discuss the link between

oxidative/electrophilic stress and the redox circuitry and consider a brief overview of

redox-sensitive transcription factors. We will then discuss the Keap1-Nrf2 axis in redox

signaling, an important target for preventive and possibly therapeutic intervention in

many types of cancers. We will also discuss the Nrf2 paradigm in gene expression,

transcriptome profiling of disparate gene categories, and the pharmacotoxicogenomic

relevance of redox-sensitive Nrf2. Finally, we will discuss the redox regulation of cell

death mechanisms.

6

1.3. Natural dietary anti-cancer chemopreventive compounds

Because carcinogenesis comprises three different stages - initiation, promotion and

progression - many potential cancer-protective (chemopreventive) agents can be

categorized broadly as blocking agents (which impede the initiation stage) or

suppressing agents (which arrest or reverse the promotion and progression of cancer),

presumably by affecting or perturbing crucial factors that control cell proliferation,

differentiation, senescence or apoptosis[2,13]. Dietary chemopreventive compounds

functioning as detoxifying enzyme inducers primarily include phenolic and sulfur-

containing compounds. Phenolic compounds may be classified into polyphenols and

flavonoids, whereas sulfur-containing compounds may be classified into

isothiocyanates and organosulfur compounds[1]. Representative examples of

polyphenols include epigallocatechin-3-gallate (EGCG) from green tea, curcumin from

turmeric, and resveratrol from grapes; whereas flavonoids are exemplified by quercetin

from citrus fruits and genistein from soy. Isothiocyanates include, amongst others,

sulforaphane (SFN) from broccoli, phenethyl isothiocyanate (PEITC) from turnips and

watercress, and allyl isothiocyanate from brusssels sprouts. Organosulfur compounds

chiefly include diallyl sulfides from garlic oil. Dietary isothiocyanates are derived in

vivo from the hydrolysis of glucosinolates present in cruciferous vegetables.

Our laboratory has worked extensively towards understanding the molecular

mechanisms in vitro and determining the chemopreventive efficacy in vivo of

polyphenols (e.g., EGCG and curcumin), and isothiocyanates (e.g., SFN and PEITC),

and combinations of these phytochemicals to elicit maximum efficacy in cancer

cells/tumorigenic tissue with minimum toxicity to normal cells. It is indeed quite

7

puzzling how these compounds can differentiate between “normal” versus “abnormal

tumor” cells in terms of signaling, gene expression and pharmacological effects. We

have shown[14] that EGCG treatment in human colon HT-29 cancer cells causes

damage to mitochondria, and that c-Jun N-terminal kinase (JNK) mediates EGCG-

induced apoptotic cell death. Similarly, it was shown[15] that PEITC can induce

apoptosis in HT-29 cells in a time- and dose-dependent manner via the mitochondrial

caspase cascade, and that the “activation” of JNK appears to be critical for the initiation

of the apoptotic processes. On the other hand, EGCG, Curcumin, SFN and PEITC

appear to “inhibit” (instead of activate) lipopolysaccharide (LPS)-induced nuclear

factor-kappaB (NFқB) activation in the same cell type - HT-29 cells - stably transfected

with an NFқB luciferase reporter construct[16]. In addition, the mechanism of action of

SFN was recently[17] attributed to the inhibition of p38 Mitogen-Activated Protein

Kinase (MAPK) isoforms which contributed to the induction of Antioxidant Response

Element (ARE)-mediated heme oxygenase-1 gene expression in human hepatoma

HepG2 cells. Besides, extracellular signal-regulated protein kinase (ERK) and JNK

pathways were activated[18] by PEITC treatment in human prostate cancer PC-3 cells.

Interestingly, a combination of PEITC and curcumin was found to have an additive

effect[19] in the induction of apoptosis in PC-3 cells stably transfected with an NFқB

luciferase reporter construct and involved an inhibition of EGFR and its downstream

signaling including PI3K and Akt. In vivo, PEITC and curcumin alone or in

combination exhibited significant cancer-preventive activities in NCr immunodeficient

(nu/nu) mice bearing subcutaneous xenografts of PC-3 cells[20]. It is tempting to

speculate that “abnormal tumor” cells such as PC-3 and HT-29 cells require the

8

overexpressed or hyper-activated NF-κB and/or EGFR for cell survival/proliferation,

whereas “normal” cells do not require these signaling molecules to do so. Blockade of

NF-κB and/or EGFR signaling by these compounds would sensitize tumor cells to die

but not the normal cells (but, instead, in normal cells, these compounds will redox-

dependently affect the Nrf2-mediated cellular detoxifying/antioxidant defense enzymes,

which will be discussed in greater detail later on), and hence the potential specificities

between “abnormal tumor” versus “normal” cells. Recently, we[3] and others[21]

showed that SFN inhibited adenoma formation in the gastrointestinal tract of genetically

mutant ApcMin/+ mice, and that the concentrations of SFN and its metabolite SFN-

GSH were found to be between 3 and 30 nmol/g (~ 3 – 30 μM), which concentrations

resembled that of the in vitro cell culture systems[22]. Interestingly, under such

conditions, using immunohistochemical (IHC) staining of the adenomas indicated that

SFN significantly suppressed the expression of phosphorylated-c-Jun-N-terminal kinase

(p-JNK), phosphorylated-extracellular signal-regulated kinases (p-ERK) and

phosphorylated-Akt (p-Akt), which were found to be highly expressed in the adenomas

of ApcMin/+ mice versus normal mucosa[3]. When the acute effect of SFN on the gene

expression profiles was investigated in the small intestine polyps of the SFN-treated

Apc+/Min mice by using Affymetrix microarray platforms, the results showed that

genes involved in apoptosis, cell growth and maintenance rather than Phase II

detoxifying genes were modulated in the polyps of the SFN-treated Apc+/Min mice.

Some of the proapoptotic genes such as MBD4 and serine/threonine kinase 17b, tumor

necrosis factor receptor superfamily member 7 and tumor necrosis factor (ligand)

superfamily member 11 were up-regulated while some pro-survival genes such as

9

cyclin-D2, integrin β1 and Wnt 9A were significantly down regulated in adenomas

treated with SFN. Importantly, two important genes involved in colorectal

carcinogenesis, arachidonate 15-lipoxygenase (15-LOX) and COX-2 were found to be

increased and decreased respectively by SFN[3]. Most recently, we found that in the

colon of the Nrf2 -/- mice challenged with the classical inflammatory agent dextran

sodium sulfate (DSS), the inflammatory proteins such as COX-2 and iNOS were

overexpressed with a concomitant decreased expression of cellular antioxidant enzymes

such as HO-1 and NQO1 [23]. These results imply that the induction of antioxidant

enzymes by dietary cancer chemopreventive compounds could potentially counteract

the oxidative/inflammatory signaling pathways as suggested by Dinkova-Kostova et al

[24].

1.4. Redox-mediated signaling

1.4.1. Oxidative stress and redox circuitry

Although oxidants are constantly generated for essential biologic functions, excess

generation or an imbalance between oxidants and antioxidants can produce a common

pathophysiologic condition known as oxidative stress[25]. Mediators of oxidative

stress, i.e., reactive oxygen species (ROS), also function as second messengers in signal

transduction. In the light of this knowledge, oxidative stress has been very remarkably

defined as perturbations in redox circuitry[6]. Indeed, proteins present on cell surfaces

and located in extracellular fluids undergo oxidation in diverse pathophysiologic

conditions, and a growing body of evidence suggests that the steady-state oxidation is

responsive to diet[25].

10

Interestingly, redox compartmentation functions as a mechanism for specificity in redox

signaling and oxidative stress, with the relative redox states[6] from most reducing to

most oxidizing being mitochondria > nuclei > cytoplasm > endoplasmic reticulum >

extracellular space. Hansen et al [6]noted that a circuitry model for redox signaling and

control has been developed based on the observations that three major thiol/disulfide

couples, namely glutathione (GSH)/glutathione disulfide (GSSG), reduced thioredoxin

[Trx-(SH)2]/oxidized thioredoxin (Trx-SS), and cysteine (Cys)/cystine (CySS) are not in

redox equilibrium and therefore could function as control nodes for many different

redox-sensitive processes. In this model, redox switches and pathways exist in parallel

circuits, with electron flow from NADPH as a central electron donor to ROS and O2 as

electron acceptors.

Given the involvement of reactive oxygen species (ROS) and reactive nitrogen species

(RNS) in a multiplicity of physiological responses through modulation of signaling

pathways, studies on RONS (reactive oxygen and nitrogen species) signaling have

received considerable attention in recent times. Forman[5] noted primarily that (i)

antioxidant enzymes are essential “turn-off’ components in signaling, (ii) spatial

relationships are probably more important in RONS signaling thatn the overall ‘redox

state’ of the cell, and (iii) deprotonation of the cysteine to form the thiolate, which can

react with RONS, occurs in specific protein sites providing specificity in signaling. The

bacterial transcription factor OxyR[8] mediates the cellular response to both ROS and

RNS by controlling the expression of the OxyR regulon, which encodes proteins

involved in H2O2 metabolism and the cytoplasmic thiol redox response. In budding

yeast, the transcription factor Yap1 (yeast AP1), which is a basic leucine zipper (bZip)

11

transcription factor, confers the cellular response to redox stress by controlling the

expression of the regulon that encodes most yeast antioxidant proteins[8]. Interestingly,

Georgiou[26] noted that, in both bacteria and yeast, the redox control networks

exemplified by OxyR and Yap1 respectively exhibit conserved dynamic features,

namely autoregulation (which in yeast is accomplished via reduction of Yap1 by

thioredoxin) and hysteresis. Indeed, the question of whether these features are intrinsic

properties of the regulatory architecture required for proper adaptation to redox stress

remains to be resolved. Jacob[27] observed that DSOs (disulfide-S-oxides) are formed

from glutathione under oxidizing conditions and specifically modulate the redox status

of thiols, indicating the existence of specialized cellular oxidative pathways. This

supports the paradigm of oxidative signal transduction and provides a general pathway

whereby ROS can convert thiols into disulfides.

1.4.2. Redox-sensitive transcription factors

Many transcription factors are redox-sensitive, including Activator protein-1 (AP-1),

Nuclear Factor kappa B (NF-κB), Nuclear Factor-E2-related factor 2 (Nrf2), p53,

glucocorticoid receptor, and others[6]. Such sensitivity involves at least two redox-

sensitive steps, one in activation of the signaling cascade and another in DNA binding,

and possibly additional redox-sensitive nuclear processes such as nuclear import and

export[6]. Nuclear and cytoplasmic redox couples perform distinct functions during

redox-sensitive transcription factor regulation.

AP-1 is responsive to low levels of oxidants resulting in AP-1/DNA binding and an

increase in gene expression. AP-1 activation is due to the induction of JNK activity by

12

oxidants resulting in the phosphorylation of serine 63 and serine 73 in the c-Jun

transactivation domain[7,28,29]. With high concentrations of oxidants, AP-1 is

inhibited and gene expression is impeded. Inhibition of AP-1/DNA interactions is

attributed to the oxidation of specific cysteine residues in c-Jun’s DNA binding region,

namely cysteine 252[7,30]. Xanthoudakis and Curran[31] reported that a DNA repair

enzyme apurinic/apyrimidinic endoculease (APE), also termed redox factor-1 (Ref-1),

possessed oxidoreductase activity and was responsible for the redox regulation of AP-1.

Oxidized AP-1 could be effectively reduced by Ref-1, restoring DNA binding activity.

Trx1 was shown to be a critical player in AP-1 regulation by virtue of its ability to

reduce oxidized Ref-1[32]. Thus, for AP-1, a nuclear pathway to reduce the Cys of the

DNA-binding domain is distinct[6] from the upstream redox events that activate the

signaling kinase pathway.

Similarly, NF-κB contains a redox-sensitive critical cysteine residue (cysteine 62) in the

p50 subunit that is involved in DNA binding[7,33]. NF-κB is normally sequestered in

the cytoplasm by IκB, but under oxidative conditions, IκB is phosphorylated by IκB

kinase (IKK), ubiquitinated and subsequently degraded. ROS production appears to be

necessary to initiate the events leading to the dissociation of the NF-κB/IκB

complex[6], but excessive ROS production (oxidative stress) results in the oxidation of

cysteine 62 which does not affect its translocation to the nucleus but rather interferes

with DNA binding and decreases gene expression[7,34]. Overexpression of Trx1

inhibited NF-κB activity, but overexpression of nuclear-targeted Trx1 enhanced NF-κB

activity[35]. These findings suggested[6] that Trx1 plays distinct roles within the

13

cytoplasm (regulation of activation and dissociation of IκB), and within the nucleus

(regulation of DNA binding).

Pivotal to the antioxidant response[36-39] typical in mammalian homeostasis and

oxidative stress is the important transcription factor Nrf2 or Nuclear Factor-E2-related

factor 2 that has been extensively studied by many research groups[1,36-43]. Nrf2 is

indispensable to cellular defense against many chemical insults of endogenous and

exogenous origin, which play major roles in the etiopathogenesis of many cancers and

inflammation-related diseases such as inflammatory bowel disease[23] and Parkinson’s

disease[44]. The role of Nrf2 as a critical redox-sensitive transcription factor will be

discussed in greater detail in the following section devoted to redox signaling with

specific emphasis on the Keap1-Nrf2 axis.

1.4.3. The Keap1-Nrf2 axis in redox signaling

Under homeostatic conditions, Nrf2 is mainly sequestered in the cytoplasm by a

cytoskeleton-binding protein called Kelch-like erythroid CNC homologue (ECH)-

associated protein 1 (Keap1)[13,40,45]. Zhang and Hannink[46] have identified two

critical cysteine residues in Keap1, namely, C273 and C288 that are required for Keap1-

dependent ubiquitination of Nrf2 as shown in Figure 1.1. They have also identified[46]

a third cysteine residue in Keap1, namely, C151, that is uniquely required for inhibition

of Keap1-dependent degradation of Nrf2 (Figure 1.1) by sulforaphane and oxidative

stress. It has also been shown[47] that Keap1 is a redox-regulated substrate adaptor

protein for a Cul3-dependent ubiquitin ligase complex that controls steady-state levels

of Nrf2 in response to cancer-preventive compounds and oxidative stress. Moreover, it

14

has been reported[48] that Keap1 regulates the oxidation-sensitive shuttling of Nrf2 into

and out of the nucleus via an active Crm1/exportin-dependent nuclear export

mechanism. When challenged with oxidative stress, Nrf2 is quickly released from

Keap1 retention and translocates to the nucleus[40,49]. We have recently identified[40]

a canonical redox-insensitive nuclear export signal (NES) (537LKKQLSTLYL546)

located in the leucine zipper (ZIP) domain of the Nrf2 protein, as well as a redox-

sensitive NES (173LLSI-PELQCLNI186) in the transactivation (TA) domain of Nrf2[50].

Once in the nucleus, Nrf2 not only binds to the specific consensus cis-element called

antioxidant response element (ARE) present in the promoter region of many

cytoprotective genes[43,45], but also to other trans-acting factors such as small Maf

(MafG and MafK)[51] that can coordinately regulate gene transcription with Nrf2. We

have also reported[41] that different segments of Nrf2 transactivation domain have

different transactivation potential; and that different mitogen-activated protein kinases

(MAPKs) have differential effects on Nrf2 transcriptional activity with ERK and JNK

pathways playing an unequivocal role in positive regulation of Nrf2 transactivation

domain activity[41]. To better understand the biological basis of signaling through

Nrf2, it has also become imperative to identify possible interacting partners of Nrf2

such as coactivators or corepressors apart from trans-acting factors such as small

Maf[51,52].

A salient feature of the canonical redox-insensitive Nrf2-NES (NESzip,

537LKKQLSTLYL546) alluded to above is its overlap with the leucine zipper domain.

This overlap implies that when Nrf2 forms heterodimers via its leucine zipper with

other bZIP proteins, such as its obligatory binding partner small MafG/K proteins, the

15

NES motif may be simultaneously masked[40]. Further, we observed[40] that Nrf2

heterodimerization via leucine zipper with MafG/K may not only enhance the DNA

binding specificity of Nrf2 but may also effectively recruit Nrf2 into the nucleus by

simultaneously masking the NES activity. Accumulating evidence shows that after

fulfilling its transactivation function, Nrf2 is destined for proteasomal degradation in the

cytoplasm, although some weak degradation activity may also exist within the nucleus.

Hence, Nrf2 signaling may be turned on and off rapidly to match the rapid changes of

the cellular redox status[40]. Interestingly, a bipartite nuclear localization signal (bNLS,

494RRRGKQKVAANQCRKRK511) as well as an NES (545LKRRLSTLYL554) have been

identified[53] in the C terminus of Nrf2, further underscoring the importance of nuclear

import and export in controlling the subcellular localization of Nrf2.

Unlike the NESzip, the new functional redox-sensitive NES located in the transactivation

(TA) domain of Nrf2 (NESTA, 173LLSIPELQCLNI186) possesses a reactive cysteine

residue (C183)[50] and exhibited a dosage-dependent nuclear translocation when

treated with sulforaphane[50]. The NESTA motif functions as a redox-sensitive switch

that can be turned on/off by oxidative signals and determines the subcellular

localization of Nrf2[50]. These discoveries suggest that Nrf2 may be able to transduce

redox signals in a Keap-1 independent manner; however, Keap1 may provide additional

regulation of Nrf2 both in basal and inducible conditions.

Li et al [50]have recently proposed a new model for Nrf2 redox signaling. As depicted

in Figure 1.1, the Nrf2 molecule possesses multivalent NES/NLS motifs, and their

relative driving forces are represented by the size and direction of the arrows. Under

unstimulated conditions (to the left of Figure 1.1), the combined nuclear exporting

16

forces of NESTA [50] and NESzip [50]may counteract the nuclear importing force of the

bNLS[53]. As a result, Nrf2 exhibits a predominantly whole cell distribution. While the

majority of Nrf2 molecules remain in the cytoplasm, the residual nuclear Nrf2 may

account for the basal or constitutive Nrf2 activities. The observation of a small

percentage of cells exhibiting nuclear and cytosolic distribution of Nrf2 may reflect the

hyper- and hypo-oxidative condition of individual cells respectively. When challenged

with oxidative stress (to the right of Figure 1.1), the redox-sensitive NESTA is disabled

but the redox-insensitive NESzip remains functional[40,50] and the bNLS motif may

remain functionally uninterrupted[50,54]. Since the driving force of the NESzip is

weaker than that of the bipartite bNLS, the nuclear importing force mediated by the

bNLS prevails and triggers Nrf2 nuclear translocation[50] as shown in Figure 1.1.

1.5. Gene expression and in vivo pharmacological effects

1.5.1. The Nrf2 paradigm in gene expression

Nrf2 knockout mice are greatly predisposed to chemical-induced DNA damage and

exhibit higher susceptibility towards cancer development in several models of chemical

carcinogenesis[43]. Observations that Nrf2-deficient mice are refractory to the

protective actions of some chemopreventive agents[43], indeed, highlight the

importance of the Keap1-Nrf2-ARE signaling pathway as a molecular target for

prevention. Hence, several studies have focused on using these Nrf2-deficient mice for

pharmacogenomic or toxicogenomic profiling of the transcriptome in response to

dietary chemopreventive agents[52,55-58] and toxicants[59].

17

1.5.2. Transcriptome profiling of putative Nrf2 coactivators and corepressors

In recent times, there is a renewed interest in dissecting the interacting partners of Nrf2

such as coactivators and corepressors which are co-regulated with Nrf2 to better

understand the biochemistry of Nrf2. In a recent microarray study[55] with Nrf2

knockout mice, it was reported that CREB-binding protein (CBP) was upregulated in

mice liver on treatment with (-)epigallocatechin-3-gallate (EGCG) in an Nrf2-

dependent manner. Katoh et al [60] showed that two domains of Nrf2 (Neh4 and Neh5)

cooperatively bind CBP and synergistically activate transcription. It was also

demonstrated[41] previously, using a Gal4-Luc reporter co-transfection assay system in

HepG2 cells, that the nuclear transcriptional coactivator CBP, which can bind to Nrf2

transactivation domain and can be activated by extracellular signal-regulated protein

kinase (ERK) cascade, showed synergistic stimulation with Raf on the transactivation

activities of both the chimera Gal4-Nrf2 (1-370) and the full-length Nrf2. In a recent

microarray study with butylated hydroxyanisole (BHA) treatment in Nrf2 knockout

mice[52], we observed the upregulation of the trans-acting factor v-maf

musculoaponeurotic fibrosarcoma oncogene family protein G avian (MafG), nuclear

receptor co-repressor 1 (Ncor1) and nuclear receptor co-repressor interacting protein

(Nrip1); as well as downregulation of the nuclear receptor co-activator 3 (Ncoa3) in an

Nrf2-dependent manner. Similarly, in another transcriptome profiling study in Nrf2

knockout mice with the Endoplasmic Reticulum (ER) stress inducer Tunicamycin[59]

that modulates the unfolded protein response (UPR), we observed the upregulation of

the P300/CBP-associated factor (P/CAF), trans-acting factor v-maf musculoaponeurotic

fibrosarcoma oncogene family, protein F (Maf F), nuclear receptor co-activator 5

18

(Ncoa5), nuclear receptor co-repressor interacting protein (Nrip1) and Smad nuclear

interacting protein 1 (Snip1) ; as well as downregulation of the src family associated

phosphoprotein 2 (Scap2) in an Nrf2-dependent manner. Although microarray

expression profiling cannot provide evidence of binding between partners, this could

potentially suggest[52] that co-repressors Ncor1 and Nrip1 and co-activator Ncoa3,

such as CBP in the previous studies, may serve as putative BHA-regulated nuclear

interacting partners of Nrf2 in eliciting the cancer chemopreventive effects of BHA; and

that co-repressor Nrip1 and co-activators P/CAF and Ncoa5 may serve as putative

Tunicamycin-regulated nuclear interacting partners of Nrf2 in eliciting the UPR-

responsive events[59] in vivo. Furthermore, induction of Nrip1 was observed in both

small intestine and liver with BHA suggesting that the Nrf2/ARE pathway may play an

important role in BHA-elicited regulation of this gene. It has also been shown

recently[61] that coactivator P/CAF could transcriptionally activate a chimeric Gal4-

Nrf2-Luciferase system containing the Nrf2 transactivation domain in HepG2 cells. In

addition, P/CAF which is known[62] to be a histoneacetyl transferase protein has

recently been shown[63] to mediate DNA damage-dependent acetylation on most

promoters of genes involved in the DNA-damage and ER-stress response, which

validates the observation[59] of P/CAF induction via Nrf2 in response to Tunicamycin-

induced ER stress.

Taken together, it is tempting to speculate that dietary chemopreventive agents such as

BHA and EGCG and toxicants such as Tunicamycin may elicit their chemopreventive

and pharmacological/toxicological events through the ARE by means of a

multimolecular complex[52,59] of coactivators and corepressors that function in concert

19

with the redox-sensitive transcription factor Nrf2 as depicted in Figure 1.2. The putative

multimolecular complex may involve Nrf2 along with the transcriptional co-repressors

Ncor1 and Nrip1 and the transcriptional co-activators Ncoa3, Ncoa5, P/CAF and CBP,

in addition to the currently known trans-acting factors such as MafG[51], with multiple

interactions between the members of the putative complex as has been shown recently

with the p160 family of proteins[61]. As shown in Figure 1.2, chemical signals

generated by dietary chemopreventive agents or toxicants may cause Nrf2 nuclear

translocation that sets in motion a dynamic machinery of coactivators and corepressors

that may form a multimolecular complex with Nrf2 to modulate transcriptional response

through the ARE. Further studies of a biochemical nature would be needed to

substantiate this hypothesis and extend our current understanding of Nrf2 regulation in

chemoprevention with BHA or EGCG and in Tunicamycin-mediated ER stress.

1.5.3. Coordinated regulation of Phase I, II and III drug metabolizing

enzyme/transporter genes via Nrf2

Dietary chemopreventives such as BHA, EGCG and Curcumin could coordinately

regulate the Phase I, II, and III xenobiotic metabolizing enzyme genes as well as

antioxidative stress genes through Nrf2-dependent pathways in vivo [52,55,56].

Interestingly, a co-ordinated response involving Phase I, II and III genes was also

observed in vivo on ER stress induction with the toxicant Tunicamycin in an Nrf2-

dependent manner[59]. The array of genes coordinately[52] modulated included Phase I

drug metabolizing enzymes, Phase II detoxification and antioxidant genes, and Phase III

transporter genes. The regulation of these genes could have significant effects on

20

prevention of tumor initiation by enhancing the cellular defense system, preventing the

activation of procarcinogens/reactive intermediates, and increasing the excretion/efflux

of reactive carcinogens or metabolites[52,56]. Indeed, the induction of Phase II

enzymes itself achieves protection against the toxic and neoplastic effects of many

carcinogens. In addition to enzymes (that conjugate to functionalized xenobiotics) such

as glutathione S-transferases and UDP-glucuronosyltransferases, a provisional and

partial list of Phase II genes might include[64] NAD(P)H:quinone reductase, epoxide

hydrolase, dihydrodiol dehydrogenase, gamma-glutamylcysteine synthetase, heme

oxygenase-1, leukotriene B4 dehydrogenase, aflatoxin B1 dehydrogenase, and ferritin.

The major Phase II genes modulated via Nrf2 by BHA, EGCG, Curcumin,

Sulforaphane, PEITC and Tunicamycin are summarized in Table 1.1.

1.5.4. Spatial and temporal control of Nrf2-mediated gene expression

It has been observed that the regulation of Nrf2-mediated gene expression takes place at

both spatial and temporal levels in the in vivo whole animals. For example, the dietary

chemopreventive BHA did not modulate the transcription of NAD(P)H:quinine

oxidoreductase (NQO1) at 3 hours; however, NQO1 was induced by BHA at 12

hours[52]. The relatively delayed induction of the NQO1 gene compared with the other

Phase II genes in response to BHA points to the possibility of differential kinetics of

BHA-regulated Phase II gene response with temporal or time-dependent specificity.

However, many other genes were modulated differentially at 3 hours in response to

BHA between small intestine and liver[52] indicating certain spatial or

tissue/compartment-dependent control of gene expression. Similar phenomena were

21

also observed in microarray studies with EGCG[55], Curcumin[56], SFN [57] and

PEITC[58]. This could be attributed to a complex of physiological factors including

partitioning across the gastrointestinal tract, intestinal transit time, uptake into the

hepatobiliary circulation, exposure parameters such as Cmax, Tmax and AUC, and the

pharmacokinetics of disposition after oral administration[52]. Other potential

complicating factor(s) could be the possibility of differential tissue/organ-dependent

expression of endogenous Keap1 and Nrf2 in conjunction with other tissue-

specific/general nuclear coregulators and corepressors[41,61,65] which could

dynamically shift the equilibrium locally in favor of or against Nrf2-mediated

transcription of different ARE-driven genes.

1.5.5. Pharmacotoxicogenomic relevance of redox-sensitive Nrf2

Recently, it was reported[66] that Nrf1, another member of the Cap’ n’ Collar (CNC)

family of basic leucine zipper proteins that is structurally similar to Nrf2, is normally

targeted to the ER membrane, and that ER stress induced by Tunicamycin in vitro may

play a role in modulating Nrf1 function as a transcriptional activator. As a protein-

folding compartment, the ER is exquisitely sensitive to alterations in homeostasis, and

provides stringent quality control systems to ensure that only correctly folded proteins

transit to the Golgi and unfolded or misfolded proteins are retained and ultimately

degraded. A number of biochemical and physiological stimuli, including perturbation in

redox status, can disrupt ER homeostasis and impose stress to the ER, subsequently

leading to accumulation of unfolded or misfolded proteins in the ER lumen[67]. The ER

has evolved highly specific signaling pathways called the unfolded protein response

22

(UPR) to cope with the accumulation of unfolded or misfolded proteins[67,68]. ER

stress stimulus by Thapsigargin has also been shown[69] to activate the c-Jun N-

terminal kinase (JNK) or stress-activated protein kinase (SAPK) that is a member of the

mitogen-activated protein kinase (MAPK) cascade[70]. Moreover, it has been reported

that the coupling of ER stress to JNK activation involves transmembrane protein kinase

IRE1 by binding to an adaptor protein TRAF2, and that IRE1 / fibroblasts were

impaired in JNK activation by ER stress[71]. It has been previously reported that

phenethyl isothiocyanate (PEITC), contained in large amounts in cruciferous vegetables

such as watercress, activates JNK1[72] , and that the activation of the antioxidant

response element (ARE) by PEITC involves both Nrf2 and JNK1[42] in HeLa cells. It

has also been demonstrated that extracellular signal-regulated kinase (ERK) and JNK

pathways play an unequivocal role in positive regulation of Nrf2 transactivation domain

activity in vitro in HepG2 cells. Although there is a growing interest amongst

researchers in targeting the UPR against cancerous tumor growth[73], there is currently

little understanding of the role of Nrf2 in modulating the UPR in vivo. Interestingly, in a

Nrf2-deficient murine model[59], glutathione peroxidase 3 (Gpx3) was upregulated and

superoxide dismutase 1 (Sod1) were downregulated by Tunicamycin in an Nrf2-

deficient manner, which can have important implications in oxidative stress-

mediated[22] pathophysiology or ER stress caused by perturbations in redox

circuitry[22,67,74]. The identification of novel molecular targets that are regulated by

the toxicant Tunicamycin via Nrf2 in vivo raises possibilities for targeting the UPR

proteins in future to augment or suppress the ER stress response and modulate disease

progression. Future toxicogenomic/toxicokinetic studies may provide new biological

23

insights into the diverse cellular and physiological processes that may be regulated by

the UPR signaling pathways in cancer pharmacology and toxicology and the role(s) of

Nrf2 in these processes.

1.5.6. Redox regulation of cellular signaling molecules leading to cell death

mechanisms

It has been demonstrated[75] that the redox regulation of platelet-derived growth factor

receptor (PDGFr) tyrosine autophosphorylation and its signaling are related to NADPH

oxidase activity through protein kinase C (PKC) and phosphoinositide-3-kinase (PI3K)

activation and H2O2 production. Cheng and Liu[76] reported that lead (Pb) increased

lipopolysaccharide (LPS)-induced liver damage in rats by modulation of tumor necrosis

factor-alpha and oxidative stress through protein kinase C and P42/44 mitogen-activated

protein kinase. Recently, it was shown[77] that Rac upregulated tissue inhibitor of

metalloproteinase-1 expression by redox-dependent activation of extracellular signal-

regulated kinase (ERK) signaling. It has been reported[78] that cancer chemoprevention

by the nitroxide antioxidant tempol acts partially via the p53 tumor suppressor.

Trachootham et al [79] have recently demonstrated a selective killing of oncogenically

transformed cells by PEITC via an ROS-mediated mechanism. Oncogenic

transformation of ovarian epithelial cells with H-Ras(V12) or expression of Bcr-Abl in

hematopoietic cells caused elevated ROS generation and rendered the malignant cells

highly sensitive to PEITC, which effectively disabled the glutathione antioxidant

system and caused severe ROS accumulation preferentially in the transformed cells due

to their active ROS output resulting in oxidative mitochondrial damage, inactivation of

24

redox-sensitive molecules, and massive cell death[79]. Xu et al [80] demonstrated an

inhibition by SFN and PEITC on NF-κB transcriptional activation as well as down-

regulation of NF-κB-regulated VEGF, cyclin D1, and Bcl-X(L) gene expression

primarily through the inhibition of IKK-beta phosphorylation, and the inhibition of IκB-

alpha phosphorylation and degradation, and decrease of nuclear translocation of p65 in

human prostate cancer PC-3 cells. Furthermore, in the same PC-3 cells, PEITC and

curcumin substantially inhibited EGFR, PI3K as well as Akt

activation/phosphorylation, and the combination of PEITC and curcumin were more

effective in the inhibition[19]. These inhibitions can also be recapitulated in vivo in PC-

3 transplanted athymic nude mice[20] Shen and Pervaiz[81] have noted a role for

redox-dependent execution in TNF receptor superfamily-induced cell death. Moreover,

oxidation of the intracellular environment of hepatocytes by ROS or redox-modulating

agents has been recently shown[82] to sensitize hepatocytes to TNF-induced apoptosis

that seems to occur only in a certain redox range (in which redox changes can inhibit

NF-κB activity but not completely inhibit caspase activity) by interfering with NF-κB

signaling pathways. Nitric oxide has been reported[83] to induce thioredoxin-1 nuclear

translocation that may be associated with the p21Ras survival pathway. Also, p53-

independent G1 cell cycle arrest of HT-29 human colon carcinoma cells by SFN was

shown[84] to be associated with induction of p21CIP1 and inhibition of expression of

cyclin D1. A small dose (10 µM) of hydrogen peroxide has been shown[85] to enhance

TNF alpha-induced cell apoptosis, upregulate Bax and downregulate Bcl-2 expression

in human vascular endothelial cell line ECV304.

25

Recently, a new functional class of redox-reactive thalidomide analogs with distinct and

selective anti-leukemic activity have been identified[86]. These agents activate nuclear

factor of activated T-cells (NFAT) transcriptional pathways while simultaneously

repressing NF-κB via a rapid intracellular amplification of ROS that is associated with

caspase-independent cell death[86]. This cytotoxicity is highly selective for transformed

lymphoid cells and preferentially targets cells in S-phase of the cell cycle[86].

Capsaicin has been shown[87] to selectively induce apoptosis in mitogen-activated or

transformed T cells with rapid increase of ROS; however, neither production of ROS

nor apoptosis was detected in capsaicin-treated resting T cells suggesting differential

signaling between activated/transformed T cells versus resting T cells. As noted

elsewhere, a combination of PEITC and curcumin has been reported[19] to suppress

epidermal growth factor receptor (EGFR) phosphorylation as well as phosphorylation of

IκB-alpha and Akt. Furthermore, Kim et al [22] reported that SFN, at low

concentrations, showed strong induction of Phase II genes that could potentially protect

cells from cytotoxic effects; however, at high concentrations (above 50µM), SFN

induced cell death with activation of caspase 3 and JNK1/2, which could be blocked by

exogenous glutathione. The extent of SFN-induced cellular stress may, thus, decide the

direction of signaling between cell survival and cell death[22]. The formation of mixed

disulfides with GSH, which is known as S-glutathionylation, is a posttranslational

modification that is emerging as an important mode of redox signaling[88]. Indeed,

redox-reactive compounds provide a new tool through which selective cellular

properties of redox status and intracellular bioactivation of potential redox-sensitive

molecular targets can be leveraged by rational combinatorial therapeutic strategies and

26

appropriate drug design to exploit cell-specific vulnerabilities for maximum drug

efficacy[86].

1.5.7. Modulation of apoptosis and cell-cycle control genes via Nrf2

Several genes related to apoptosis and cell-cycle control that are critical in the

etiopathogenesis of many cancers have been shown[52,55-59] to be regulated through

Nrf2 in response to several dietary chemopreventive agents and toxicants, which are far

too many to be discussed here at least in normal murine tissues. The reader is referred to

references[52,55-59] for a comprehensive list of these genes. Future studies would

ascertain whether Nrf2 would be required for regulation of these apoptosis and cell-

cycle control genes in tumor tissues/cells.

1.6. Integrated systems biology approach to cancer chemoprevention

Rhodes and Chinnaiyan [89] noted that although microarray profiling can to some

extent decipher the molecular heterogeneity of cancer, integrative analyses that evaluate

cancer transcriptome data in the context of other data sources are more capable of

extracting deeper biological insight from the data. Indeed, integrative computational

and analytical approaches, including Transcription Factor Binding Site (TFBS)-

association signature analyses and transcriptional network analyses are a step in this

direction with the ultimate aim of enhancing our understanding of prognostic

biomarkers and key signaling pathways that are important in cancer progression, thus

delineating functionally relevant targets for chemopreventive or therapeutic

intervention. Thus, the elucidation of several target hubs in the latent structure of

27

biological networks and the delineation of key TFBS-association signatures, would help

enhance our understanding of regulatory nodes that might be central to identifying key

players in cellular signal transduction cascades that are important in the pathogenesis of

cancer.

1.7. Concluding Remarks

In summary, electrophilic stress, oxidative stress or perturbations in redox circuitry can

have a profound influence on the direction of signaling between cell survival and cell

death. The potential involvement of cellular redox-sensitive transcription factors can

modulate gene expression events and pharmacotoxicologic responses in diseases like

cancer, thus, providing new molecular targets for preventive or therapeutic intervention.

Indeed, controlled nutritional studies in future may specifically utilize extracellular

redox measurements to explore mechanistic links between diet, health status, and

diseases[25]. Taken together, redox-mediated signaling is an important mechanism for

the cancer chemopreventive effects elicited by dietary anti-cancer chemopreventive

compounds. Future studies will likely provide additional insights into the intricacies of

the redox paradigm in signal transduction pathways with respect to the etiopathogenesis

of cancer and its potential for chemopreventive intervention.

In addition, pharmacogenomic profiling of cancer has recently seen much activity with

the accessibility of the newest generation of high-throughput platforms and

technologies. A myriad of mechanistic studies have been devoted to identifying dietary

factors that can help prevent cancer, with evidence gleaned from epidemiologic studies

revealing an inverse correlation between the intake of cruciferous vegetables and the

28

risk of certain types of cancer. To develop a comprehensive understanding of cancer

pathogenesis, and potential for chemopreventive intervention with dietary factors, an

integrated approach that encompasses both pharmacogenomic and mechanistic aspects

is desirable.

1.8. Acknowledgements

The authors are grateful to all members of the Kong laboratory as well as researchers in

the scientific community whose works have been cited here. The authors also regret the

inability to include many other excellent relevant citations in the interests of

comprehensive abridgement. Works described here were supported in part by RO1-

CA094828, RO1-CA092515, RO1-CA073674 and R01-CA118947 to Ah-Ng Tony

Kong from the National Institutes of Health (NIH).

29

Figure 1.1. The Keap1-Nrf2 axis in redox signaling – Proposed model for Nrf2

redox signaling.

Nrf2 is sequestered to Keap1, a cytosolic actin-binding protein. Critical cysteine

residues in Keap1 (C273[46] and C288[46]), are required for Keap1-dependent

ubiquitination of Nrf2. A third cysteine residue in Keap1 (C151[46]), is uniquely

required for inhibition of Keap1-dependent degradation of Nrf2. The Nrf2 molecule

possesses multivalent NES/NLS motifs (dotted lines from Nrf2 molecule diverge to

30

encompass the dissected individual motifs), and their relative driving forces are

represented by the size and direction of the arrows. Under unstimulated conditions (to

the left of Figure 1), the combined nuclear exporting forces of NESTA [50] and NESzip

[50] may counteract the nuclear importing force of the bNLS[53]. As a result, Nrf2

exhibits a predominantly whole cell distribution[50]. While the majority of Nrf2

molecules remain in the cytoplasm, the residual nuclear Nrf2 may account for the basal

or constitutive Nrf2 activities. When challenged with oxidative stress (to the right of

Figure 1), the redox-sensitive NESTA is disabled but the redox-insensitive NESzip

remains functional[40,50] and the bNLS motif may remain functionally

uninterrupted[50,54]. Since the driving force of the NESzip is weaker than that of the

bipartite bNLS, the nuclear importing force mediated by the bNLS prevails and triggers

Nrf2 nuclear translocation as shown[50].

31

Figure 1.2. The Nrf2 paradigm in gene expression - Proposed model for a

multimolecular coactivator-corepressor complex that elicits transcriptional events

through the ARE.

Chemical signals generated by dietary chemopreventive agents or toxicants may cause

Nrf2 nuclear translocation that sets in motion a dynamic machinery of coactivators and

corepressors[52,59] that may form a multimolecular complex with Nrf2 to modulate

transcriptional response through the ARE. The putative multimolecular complex may

32

involve the transcriptional co-repressors Ncor1[52] and Nrip1[52,59], and the

transcriptional co-activators Ncoa3[52], Ncoa5[59], P/CAF[59], CBP[55], and

MafG[52], with multiple interactions between the members of the putative complex that

function in concert with the redox-sensitive transcription factor Nrf2 to elicit the

chemopreventive and pharmacological/toxicological events through the ARE.

33

CHAPTER 2

Pharmacogenomics of Phenolic Antioxidant Butylated hydroxyanisole (BHA)

in the Small Intestine and Liver of Nrf2 Knockout and C57BL/6J Mice4,5,6

2.1. Abstract

This objective of this study was to investigate the pharmacogenomics and the spatial

regulation of global gene expression profiles elicited by cancer chemopreventive agent

butylated hydroxyanisole (BHA) in mouse small intestine and liver as well as to

identify BHA-modulated Nuclear Factor-E2-related factor 2 (Nrf2)–dependent genes.

C57BL/6J (+/+; wildtype) and C57BL/6J/Nrf2(–/–; knockout) mice were administered a

single 200 mg/kg oral dose of BHA or only vehicle. Both small intestine and liver were

collected at 3 h after treatment and total RNA was extracted. Gene expression profiles

were analyzed using 45,000 Affymetrix mouse genome 430 2.0 array and GeneSpring

7.2 software. Microarray results were validated by quantitative real-time reverse

transcription-PCR analyses. Clusters of genes that were either induced or suppressed

more than two-fold by BHA treatment compared with vehicle in C57BL/6J/Nrf2(-/-;

knockout) and C57BL/6J/Nrf2(+/+; wildtype) mice genotypes were identified. Amongst

4Work described in this chapter has been published as Nair, S., Kong, A.-N. T., Pharm Res. 2006 Nov;23(11):2621-37. 5Keywords : Butylated hydroxyanisole, Nuclear Factor-E2-related factor 2, microarray, chemoprevention, global gene expression profiles. 6Abbreviations : BHA, Butylated hydroxyanisole; Nrf2, Nuclear Factor-E2-related factor 2; Mapk, Mitogen-activated protein kinase; ARE, Antioxidant response element.

34

these, in small intestine and liver, 1490 and 493 genes respectively were identified as

Nrf2-dependent and upregulated, and 1090 and 824 genes respectively as Nrf2-

dependent and downregulated. Based on their biological functions, these genes can be

categorized into ubiquitination/proteolysis, apoptosis/cell cycle, electron transport,

detoxification, cell growth/differentiation, transcription factors/interacting partners,

kinases and phosphatases, transport, biosynthesis/metabolism, RNA/protein processing

and nuclear assembly, and DNA replication genes. Phase II detoxification/antioxidant

genes as well as novel molecular target genes, including putative interacting partners of

Nrf2 such as nuclear corepressors and coactivators, were also identified as Nrf2-

dependent genes. The identification of BHA-regulated and Nrf2-dependent genes not

only provides potential novel insights into the gestalt biological effects of BHA on the

pharmacogenomics and spatial regulation of global gene expression profiles in cancer

chemoprevention, but also points to the pivotal role of Nrf2 in these biological

processes.

2.2. Introduction

The phenolic antioxidant butylated hydroxyanisole (BHA) is a commonly used food

preservative with broad biological activities [90], including protection against acute

toxicity of chemicals, modulation of macromolecule synthesis and immune response,

induction of phase II detoxifying enzymes, and, indeed, its potential tumor-promoting

activities. Whereas the potential cytotoxicity of BHA has been partially attributed to

reactive intermediates [90,91], BHA has also been shown to shift cell death from

necrosis to apoptosis [92,93] and to inhibit mitochondrial complex I and lipoxygenases

35

[92]. A chemopreventive role for BHA is reiterated by the induction of A5 subunit of

GST in rat liver immunoblotting experiments [94]. BHA has also been reported to

increase the levels of liver glutathione and the activity of hepatic cytosolic gamma-

glutamylcysteine synthetase [92]. Moreover, BHA has been shown to be an effective

inhibitor of 7,12-dimethylbenz(a) anthracene-induced mammary carcinogenesis [95] in

Sprague-Dawley rats; and is effective in the chemoprevention [96] of 1,2-

dimethylhydrazine-induced large bowel neoplasms. In addition, BHA in diet has been

demonstrated [97] to inhibit the initiation phase of 2-acetylaminofluorene and aflatoxin

B1 hepatocarcinogenesis in rats. We have previously demonstrated [98] that the

cytotoxicity of BHA is due to the induction of apoptosis that is mediated by the direct

release of cytochrome c and the subsequent activation of caspases.

Pivotal to the antioxidant response [36-39] typical in mammalian homeostasis and


factor 2 that has been extensively studied by many research groups [36-39] including

this laboratory [1,40-42]. Under homeostatic conditions, Nrf2 is mainly sequestered in

the cytoplasm by a cytoskeleton-binding protein called Kelch-like erythroid CNC

homologue (ECH)-associated protein 1 (Keap1) [13,40,45]. When challenged with

oxidative stress, Nrf2 is quickly released from Keap1 retention and translocates to the

nucleus [40,49]. We have recently identified [40] a canonical redox-insensitive nuclear

export signal (NES) (537LKKQLSTLYL546) located in the leucine zipper (ZIP) domain

of the Nrf2 protein. Once in the nucleus, Nrf2 not only binds to the specific consensus

cis-element called antioxidant response element (ARE) present in the promoter region

of many cytoprotective genes [41,43,45], but also to other trans-acting factors such as

36

small Maf (MafG and MafK) [51] that can coordinately regulate gene transcription with

Nrf2. We have previously demonstrated [90,99] that BHA is capable of activating

distinct mitogen-activated protein kinases (MAPKs) such as extracellular signal-

regulated protein kinase 2 (ERK2), and c-Jun N-terminal kinase 1 (JNK1). We have

also reported [41] that different segments of Nrf2 transactivation domain have different

transactivation potential; and that different MAPKs have differential effects on Nrf2

transcriptional activity with ERK and JNK pathways playing an unequivocal role in

positive regulation of Nrf2 transactivation domain activity [41]. To better understand

the biological basis of signaling through Nrf2, it has also become imperative to identify

possible interacting partners of Nrf2 such as coactivators or corepressors apart from

trans-acting factors such as small Maf [51].



carcinogenesis [43]. Observations that Nrf2-deficient mice are refractory to the

protective actions of some chemopreventive agents [43], indeed, highlight the

importance of the Keap1-Nrf2-ARE signaling pathway as a molecular target for

prevention. In the present study, we have investigated, by microarray expression

profiling, the global gene expression profiles elicited by oral administration of BHA in

small intestine and liver of Nrf2 knockout (C57BL/6J/Nrf2-/-) and wild type

(C57BL/6J) mice to enhance our understanding of BHA-regulated cancer

chemopreventive effects mediated through Nrf2. We have identified clusters of BHA-

modulated genes that are Nrf2-dependent in small intestine and liver and categorized

them based on their biological functions. The identification of BHA-regulated Nrf2-

37

dependent genes will yield valuable insights into the role of Nrf2 in BHA-modulated

gene regulation and cancer chemopreventive effects. This study also enables the

identification of novel molecular targets for BHA-mediated chemoprevention that are

regulated by Nrf2. The current study is also the first to investigate the global gene

expression profiles elicited by BHA in an in vivo murine model where the role of Nrf2

is also examined.

2.3. Materials and Methods

Animals and Dosing : The protocol for animal studies was approved by the

Rutgers University Institutional Animal Care and Use Committee (IACUC). Nrf2

knockout mice Nrf2 (-/-) (C57BL/SV129) have been described previously.[100] Nrf2 (-

/-) mice were backcrossed with C57BL/6J mice (The Jackson Laboratory, ME USA).

DNA was extracted from the tail of each mouse and genotype of the mouse was

confirmed by polymerase chain reaction (PCR) by using primers (3’-primer, 5’-GGA

ATG GAA AAT AGC TCC TGC C-3’; 5’-primer, 5’-GCC TGA GAG CTG TAG GCC

C-3’; and lacZ primer, 5’-GGG TTT TCC CAG TCA CGA C-3’). Nrf2(-/-) mice-

derived PCR products showed only one band of ~200bp, Nrf2 (+/+) mice-derived PCR

products showed a band of ~300bp while both bands appeared in Nrf2(+/-) mice PCR

products. Female C57BL/6J/Nrf2(-/-) mice from third generation of backcrossing were

used in this study. Age-matched female C57BL/6J mice were purchased from The

Jackson Laboratory (Bar Harbor, ME). Mice in the age-group of 9-12 weeks were

housed at Rutgers Animal Facility with free access to water and food under 12 h

light/dark cycles. After one week of acclimatization, the mice were put on AIN-76A

38

diet (Research Diets Inc. NJ USA) for another week. The mice were then administered

BHA (Sigma-Aldrich, St.Louis, MO) at a dose of 200 mg/kg (dissolved in 50% PEG

400 solution at a concentration of 20 mg/ml)) by oral gavage. The control group

animals were administered only vehicle (50% PEG 400 solution). Each treatment was

administered to a group of four animals for both C57BL/6J and C57BL/6J/Nrf2(-/-)

mice. Mice were sacrificed 3h after BHA treatment or 3 h after vehicle treatment

(control group). Livers and small intestines were retrieved and stored in RNA Later

(Ambion, Austin,TX) solution.

Sample Preparation for Microarray Analyses: Total RNA from liver and

small intestine tissues were isolated by using a method of TRIzol (Invitrogen, Carlsbad,

CA) extraction coupled with the RNeasy kit from Qiagen (Valencia, CA). Briefly,

tissues were homogenized in trizol and then extracted with chloroform by vortexing. A

small volume (1.2 ml) of aqueous phase after chloroform extraction and centrifugation

was adjusted to 35% ethanol and loaded onto an RNeasy column. The column was

washed, and RNA was eluted following the manufacturer’s recommendations. RNA

integrity was examined by electrophoresis, and concentrations were determined by UV

spectrophotometry.

Microarray Hybridization and Data Analysis: Affymetrix (Affymetrix, Santa

Clara, CA) mouse genome 430 2.0 array was used to probe the global gene expression

profiles in mice following BHA treatment. The mouse genome 430 2.0 Array is a high-

density oligonucleotide array comprised of over 45,101 probe sets representing over

39

34,000 well-substantiated mouse genes. The library file for the array is available at

http://www.affymetrix.com/support/technical/libraryfilesmain.affx. After RNA

isolation, all the subsequent technical procedures including quality control and

concentration measurement of RNA, cDNA synthesis and biotin-labeling of cRNA,

hybridization and scanning of the arrays, were performed at CINJ Core Expression

Array Facility of Robert Wood Johnson Medical School (New Brunswick, NJ). Each

chip was hybridized with cRNA derived from a pooled total RNA sample from four

mice per treatment group, per organ, and per genotype (a total of eight chips were used

in this study) (Figure 2.2). Briefly, double-stranded cDNA was synthesized from 5 µg

of total RNA and labeled using the ENZO BioArray RNA transcript labeling kit (Enzo

Life Sciences, Inc.,Farmingdale, NY, USA) to generate biotinylated cRNA. Biotin-

labeled cRNA was purified and fragmented randomly according to Affymetrix’s

protocol. Two hundred microliters of sample cocktail containing 15 μg of fragmented

and biotin-labeled cRNA was loaded onto each chip. Chips were hybridized at 45°C for

16 h and washed with fluidics protocol EukGE-WS2v5 according to Affymetrix’s

recommendation. At the completion of the fluidics protocol, the chips were placed into

the Affymetrix GeneChip Scanner where the intensity of the fluorescence for each

feature was measured. The expression value (average difference) for each gene was

determined by calculating the average of differences in intensity (perfect match

intensity minus mismatch intensity) between its probe pairs. The expression analysis

file created from each sample (chip) was imported into GeneSpring 7.2 (Agilent

Technologies, Inc., Palo Alto, CA) for further data characterization. Briefly, a new

experiment was generated after importing data from the same organ in which data was

40

normalized by array to the 50th percentile of all measurements on that array. Data

filtration based on flags present in at least one of the samples was first performed, and a

corresponding gene list based on those flags was generated. Lists of genes that were

either induced or suppressed more than two fold between treated versus vehicle group

of same genotype were created by filtration-on-fold function within the presented flag

list. By use of color-by-Venn-Diagram function, lists of genes that were regulated more

than two fold only in C57BL/6J mice in both liver and small intestine were created.

Similarly, lists of gene that were regulated over two fold regardless of genotype were

also generated.

Quantitative Real-time PCR for Microarray Data Validation: To validate

the microarray data, 13 genes of interest were selected from various categories for

quantitative real-time PCR analyses. Glyceraldehyde-3-phosphate-dehydrogenase

(GAPDH) served as the “housekeeping” gene. The specific primers for these genes

listed in Table 2.1 were designed by using Primer Express 2.0 software (Applied

Biosystems, Foster City, CA) and were obtained from Integrated DNA Technologies,

Coralville, IA. The specificity of the primers was examined by a National Center for

Biotechnology Information Blast search of the mouse genome. Instead of using pooled

RNA from each group, RNA samples isolated from individual mice as described earlier

were used in real-time PCR analyses. For the real-time PCR assays, briefly, first-strand

cDNA was synthesized using 4μg of total RNA following the protocol of SuperScript

III First-Strand cDNA Synthesis System (Invitrogen) in a 40 μl reaction volume. The

PCR reactions based on SYBR Green chemistry were carried out using 100 times

41

diluted cDNA product, 60 nM of each primer, and SYBR Green master mix (Applied

Biosystems, Foster City, CA) in 10 μl reactions. The PCR parameters were set using

SDS 2.1 software (Applied Biosystems, Foster City, CA) and involved the following

stages : 50°C for 2min, 1 cycle; 95°C for 10 mins, 1 cycle; 95°C for 15 secs 55 °C

for 30 secs 72°C for 30 secs, 40 cycles; and 72°C for 10 mins, 1 cycle. Incorporation

of the SYBR Green dye into the PCR products was monitored in real time with an ABI

Prism 7900HT sequence detection system, resulting in the calculation of a threshold

cycle (CT) that defines the PCR cycle at which exponential growth of PCR products

begins. The carboxy-X-rhodamine (ROX) passive reference dye was used to account for

well and pipetting variability. A control cDNA dilution series was created for each gene

to establish a standard curve. After conclusion of the reaction, amplicon specificity was

verified by first-derivative melting curve analysis using the ABI software and the

integrity of the PCR reaction product and absence of primer dimers was ascertained.

The gene expression was determined by normalization with control gene GAPDH.

Statistics : In order to validate the results, the correlation between

corresponding microarray data and real-time PCR data was evaluated by the ‘coefficient

of determination’, r2 = 0.89.

42

2.4. Results

2.4.1. BHA-Modulated Gene Expression Patterns in Mouse Small Intestine and

Liver

Subsequent to data normalization, 50.5% (22,779) of the probes passed the filtration

based on flags present in at least one of four small intestine sample arrays depicted in

Figure 2.2. Amongst these probes, 13.86% and 11.69% of probes were induced and

suppressed over two fold respectively regardless of genotype. Expression levels of 2580

probes were either elevated (1490) or suppressed (1090) over two fold by BHA only in

the wild-type mice, while 3243 probes were either induced (1669) or inhibited (1574)

over two fold by BHA only in the Nrf2(–/–) mice small intestine (Figure 2.3A).

Similarly, changes in gene expression profiles were also observed in mice liver.

Overall, the expression levels of 52.79% (23,809) probes were detected in least in one

of four liver sample arrays depicted in Figure 2.2. Amongst these probes, 6.29% and

5.94% of probes were induced and suppressed over two fold respectively regardless of

genotype. In comparison with the results from small intestine sample arrays, a smaller

proportion (1317) of well-defined genes were either elevated (493) or suppressed (824)

over two fold by BHA in wild-type mice liver alone; whereas 1596 well-defined genes

were induced (1005) or inhibited (591) in Nrf2(–/–) mice liver. (Figure 2.3B).

2.4.2. Quantitative Real–Time PCR Validation of Microarray Data

To validate the data generated from the microarray studies, several genes from different

categories (Table 2.1) were selected to confirm the BHA-regulative effects by the use of

quantitative real-time PCR analyses as described in detail under Materials and Methods.

43

After ascertaining the amplicon specificity by first-derivative melting curve analysis,

the values obtained for each gene were normalized by the values of corresponding

GAPDH expression levels. The fold changes in expression levels of treated samples

over control samples were computed by assigning unit value to the control (vehicle)

samples. Computation of the correlation statistic showed that the data generated from

the microarray analyses are well-correlated with the results obtained from quantitative

real-time PCR (coefficient of determination, r2 = 0.89, Figure 2.4).

2.4.3. BHA-Induced Nrf2-Dependent Genes in Small Intestine and Liver

Genes that were induced only in wild-type mice, but not in Nrf2(–/–) mice, by BHA

were designated as BHA-induced Nrf2-dependent genes. Based on their biological

functions, these genes were classified into categories, including ubiquitination and

proteolysis, electron transport, detoxification enzymes, transport, apoptosis and cell

cycle control, cell adhesion, kinases and phosphatases, transcription factors and

interacting partners, RNA/Protein processing and nuclear assembly, biosynthesis and

metabolism, cell growth and differentiation, and G protein-coupled receptors (Table 2.2

lists a subset of these genes relevant to our interest).

Gene expression in small intestine in response to BHA treatment was more sensitive

than that elicited in the liver with a larger number of Nrf2-dependent genes being

upregulated in the former. The category of transcription factors and interacting partners

predominated the upregulated genes followed by kinases and phosphatases. In the

former category, a number of interesting transcription factors were identified as BHA-

regulated Nrf2-dependent genes. In the small intestine, these primarily included insulin-

44

like growth factor 2 (Igf2), Jun oncogene (Jun), Notch gene homolog 4 (Drosophila)

(Notch 4), nuclear receptor co-repressor (Ncor1), nuclear receptor interacting protein 1

(Nrip1), serum response factor binding protein 1 (Srfbp1), Spred-1 (Spred1), suppressor

of cytokine signaling 5 (Socs5), thyroid hormone receptor beta (Thrb), transforming

growth factor, beta receptor 1(Tgfbr1), transducer of ERBB2, 2 (Tob2), members 19

and 23 of tumor necrosis factor superfamily (Tnfrsf19 and Tnfrsf23), v-maf

musculoaponeurotic fibrosacroma oncogene family, protein G (avian) (MafG), and

wingless-type MMTV integration site 9B (Wnt9b). Similarly, the major BHA-regulated

Nrf2-dependent transcription factors identified in the liver included Activating signal

cointegrator 1 complex subunit 2 (Ascc2), Eph receptor A3 (Epha3), Eph receptor B1

(Ephb1), fos-like antigen 2 (Fosl2), insulin-like growth factor 2 receptor (Igf2r), nuclear

receptor interacting protein 1 (Nrip1), RAB4A member RAS oncogene family (Rab4a),

reticuloendotheliosis oncogene (Rel), and transcription factor AP-2 beta (Tcfap2b).

Interestingly, induction of Nrip1 was observed in both small intestine and liver

suggesting that the Nrf2/ARE pathway may play a dominant role in BHA-elicited

regulation of this gene.

Amongst the kinases and phosphatases in small intestine, the highest expression was

seen with microtubule associated serine/threonine kinase-like (Mastl). Several

important members of the mitogen-activated protein kinase (Mapk) pathway activating

the Nrf2/ARE signaling were also induced in the small intestine including Mapk8,

Mapk6, Map3k9, Map4k4 and Map4k5 with strongest induction seen with Mapk8.

Moreover, induction was also observed of Janus kinase 2 (Jak2), types 1 and 2 of

neurotrophic tyrosine kinase, receptor (Ntrk1 and Ntrk2), tyrosine kinase, non-

45

receptor,1 (Tnk1). Comparable expression was noted of different isoforms of protein

kinases including epsilon, eta, and cAMP dependent regulatory, type II alpha (Prkce,

Prkch and Prkar2a respectively). Regulatory subunits of protein phosphatase 1

(Ppp1r14a) and 2 (Ppp2r5e) and catalytic subunit of protein phosphatase 3, beta isoform

(Ppp3cb) were also upregulated in the small intestine as BHA-regulated and Nrf2-

dependent genes. In the liver, the genes induced in the same category included

diacylglycerol kinase kappa (Dagkκ) , inhibitor of kappaB kinase gamma (Iκbkg),

protein kinase, cAMP dependent regulatory, type I, alpha (Prkar1a), protein tyrosine

phosphatase (Ptp), and putative membrane-associated guanylate kinase 1 (Magi-1)

mRNA, alternatively spliced c form (Baiap1).

Representative genes induced by BHA in an Nrf2-dependent manner in the category of

apoptosis and cell cycle control genes included members of the caspase cascade as well

as cyclins G1 and T2 in small intestine; and growth arrest and DNA-damage-inducible

45 alpha (Gadd45a) in liver. Several important Nrf2-dependent detoxifying genes were

also upregulated by BHA including glutamate-cysteine ligase, catalytic subunit (Gclc)

in both small intestine and liver, glutathione S-transferase, mu 1 (Gstm1) and mu 3

(Gstm3) isoforms in small intestine and liver respectively, and heme oxygenase

(decycling) 1 (Hmox1) in liver. BHA could also modulate many other important

categories of genes in an Nrf2-dependent manner. Salient amongst them were the

biosynthesis and metabolism genes, G-protein coupled receptors, RNA/protein

processing and nuclear assembly, ubiquitination and proteolysis, and transport genes.

Major transporters induced included the multidrug resistance associated proteins Mrp1

46

and 3 and Mdr1. Also induced were the sodium/glucose cotransporter (Slc5a12) and ion

channels for sodium, potassium and chloride ions.

2.4.4. BHA-Suppressed Nrf2-Dependent Genes in Small Intestine and Liver

As shown in Table 2.3, which lists a subset of genes relevant to our interest, BHA

treatment also inhibited the expression of many genes falling into similar functional

categories in an Nrf2-dependent manner, although the number of genes was smaller.

Notably, the category of transcription factors and interacting partners remained the

largest amongst these genes with Nuclear receptor coactivator 3 (Ncoa3), and src family

associated phosphoprotein 1 (Scap1) being among the genes suppressed by BHA and

co-regulated with Nrf2 in small intestine; and epidermal growth factor receptor pathway

substrate 15 (Eps15) and Hypoxia inducible factor 1, alpha subunit (Hif1a) being

suppressed in the liver.

Amongst the kinases and phosphatases, BHA suppressed, in small intestine, the

expression of G protein-coupled receptor kinase 5 (Grk5), glycogen synthase kinase 3

beta (Gsk3β), Mapk-activated protein kinase 5 (Mapkapk5), Mapk associated protein 1

(Mapkap1), p21-activated kinase 3 (Pak3), and ribosomal protein S6 kinase,

polypeptide 4 (Rps6ka4). In the liver, BHA suppressed, in an Nrf2-dependent manner,

genes such as Map2k7, phosphoinositide-3-kinase, regulatory subunit 5, p101 (Pik3r5),

Ribosomal protein S6 kinase, polypeptide 5 (Rps6ka5) and microtubule associated

serine/threonine kinase family member 4 (Mast4) amongst others.

Major genes down-regulated by BHA in an Nrf2-dependent manner in the category of

apoptosis and cell cycle control included B-cell leukemia/lymphoma 2 (Bcl2), breast

47

cancer 1 (Brca1), and Cyclin I in liver. Among the transport genes, the fatty acid

binding protein 6, ileal (gastrotropin) (Fabp6) was suppressed in small intestine by

BHA. In the category of electron transport genes, representative genes included the

cytochrome c oxidase, subunit VIIa 1 (Cox7a1) which was inhibited in both small

intestine and liver, and thioredoxin reductase 2 (Txnrd2) which was suppressed in liver.

Besides, members of the cytochrome P450 family such as Cyp3a44 in small intestine,

and Cyp21a1 and Cyp7a1 in liver were also down-regulated by BHA in an Nrf2-

dependent manner in the same category. Other categories of BHA-suppressed genes

including ubiquitination and proteolysis, RNA/protein processing and nuclear assembly,

biosynthesis and metabolism, and cell growth and differentiation were also identified as

regulated through Nrf2.

2.5. Discussion

Since BHA was first introduced as a food preservative back in the 1960s, it has attracted

a lot of attention and debate because of its potentially diverse biological effects on the

health of humans including its potential cancer chemopreventive effects. Although

extensive studies have been conducted to define the biological activities of BHA, and a

growing body of evidence has been accumulated, a comprehensive definition of the

potential cellular targets of BHA that trigger important signal transduction pathways

remains a challenge that has yet to be undertaken. Indeed, the molecular basis and the

mechanisms of action of BHA are not quite yet fully understood [90]. Transcription

factor Nrf2 or Nuclear Factor-E2 -related factor 2 is indispensable to cellular defense

against many chemical insults of endogenous and exogenous origin[43], which play

48

major roles in the etiopathogenesis of many cancers. Pivotal to this role of Nrf2 is the

antioxidant response element (ARE) present in the promoter regions of many

cytoprotective genes [43,45]. Indeed, Nrf2 (-/-; deficient) mice, which are highly

susceptible to cancer development, are known to be refractory to the protective actions

of some cancer chemopreventive agents [43]. It is, therefore, of interest to investigate

the role of Nrf2 in BHA-elicited global gene expression profiles in vivo in order to

extend the latitude of current understanding on the Nrf2-ARE signaling pathway that

has emerged as an important molecular target for cancer chemoprevention. To our

knowledge, this is the first attempt to elucidate, by microarray expression profiling, the

gestalt genomic basis of BHA-regulated Nrf2-dependent cancer chemoprevention in

vivo in an Nrf2-deficient murine model.

In the continuing quest to unravel the complex secrets of the biology of Nrf2 in cancer

chemoprevention, there is renewed interest in dissecting the interacting partners of Nrf2

such as coactivators and corepressors which are co-regulated with Nrf2. In a recent

microarray study [55], we have reported that CREB-binding protein (CBP) was

upregulated in mice liver on treatment with (-)epigallocatechin-3-gallate (EGCG) in an

Nrf2-dependent manner. We have also demonstrated [41] previously, using a Gal4-Luc

reporter co-transfection assay system in HepG2 cells, that the nuclear transcriptional

coactivator CBP, which can bind to Nrf2 transactivation domain and can be activated by

extracellular signal-regulated protein kinase (ERK) cascade, showed synergistic

stimulation with Raf on the transactivation activities of both the chimera Gal4-Nrf2 (1-

370) and the full-length Nrf2. In the current study, we observed the upregulation of the

trans-acting factor v-maf musculoaponeurotic fibrosarcoma oncogene family, protein G,

49

avian (MafG), nuclear receptor co-repressor 1 (Ncor1) and nuclear receptor co-repressor

interacting protein (Nrip1); as well as downregulation of the nuclear receptor co-

activator 3 (Ncoa3) in an Nrf2-dependent manner. Although microarray expression

profiling cannot provide evidence of binding between partners, this is the first

investigation to potentially suggest that co-repressors Ncor1 and Nrip1 and co-activator

Ncoa3, such as CBP in our previous studies, may serve as putative BHA-regulated

nuclear interacting partners of Nrf2 in eliciting the cancer chemopreventive effects of

BHA. Furthermore, induction of Nrip1 was observed in both small intestine and liver

suggesting that the Nrf2/ARE pathway may play an important role in BHA-elicited

regulation of this gene. Taken together, it is tempting to speculate that the BHA-

regulated chemopreventive effects through the ARE may be regulated by a

multimolecular complex, which involves Nrf2 along with the transcriptional co-

repressors Ncor1 and Nrip1 and the transcriptional co-activator Ncoa3, in addition to

the currently known trans-acting factors such as MafG [51], with multiple interactions

between the members of the putative complex as we have shown recently with the p160

family of proteins [101]. Further studies of a biochemical nature would be needed to

substantiate this hypothesis and extend our current understanding of Nrf2 regulation in

chemoprevention with BHA.

The major detoxification genes induced in this study included glutamate-cysteine ligase,

catalytic subunit (Gclc) in both small intestine and liver, glutathione S-transferase, mu 1

(Gstm1) and mu 3 (Gstm3) isoforms in small intestine and liver respectively, and heme

oxygenase (decycling) 1 (Hmox1) in liver. Since Nrf2 binds to the cis-acting ARE and

induces the expression of many important Phase II detoxification and antioxidant genes

50

[36,43,45,49], and since BHA is known to induce Phase II genes, the current study on

spatial regulation in small intestine and liver of BHA-regulated chemopreventive effects

via Nrf2 showing the upregulation of Phase II detoxification genes is, indeed, consistent

with previous reports[37,102-105] and also validates our results from a biological

perspective. Indeed, we were able to detect the presence of NAD(P)H:quinine

oxidoreductase (NQO1) gene induction in qRT-PCR experiments performed in another

study at a 12 hour time-point (data not shown) suggesting that there may be a relatively

delayed induction of the NQO1 gene compared with the other Phase 2 genes in response

to BHA and possible differential kinetics of BHA-regulated Phase 2 gene response.

Interestingly, genes involved in Phase I drug metabolism as well as Phase III drug

transport were also regulated by BHA via Nrf2. The Phase I drug-metabolizing

enzymes (DME’s) identified here included cytochrome P450 family members Cyp7a1

and Cyp21a1 suppressed in liver and Cyp3a44 suppressed in small intestine. The roles

played by these Phase I DME’s in BHA-elicited cancer chemopreventive effects,

however, remain presently unknown. Several transport-related genes were also

identified in this study for the first time as both BHA-regulated and Nrf2-dependent.

Amongst the upregulated transporters were the ATP-binding cassette superfamily

members belonging to MDR/TAP, MRP or ALD subfamilies such as MDR1a/P-

glycoprotein (Abcb1a) and MRP 1 (Abcc1) in small intestine; and MRP3 (Abcc3) in

the liver. The ALD member responsible for peroxisomal import [106] of fatty acids

(Abcd3) was upregulated in the liver, whereas the fatty acid binding protein 6, ileal

(gastrotropin) (Fabp6) was suppressed in small intestine, suggesting a putative role for

BHA and Nrf2 co-regulation of lipid pathways in chemoprevention that has never been

51

examined. Also identified for the first time as transport genes upregulated by BHA via

Nrf2 were members of the solute carrier family such as genes encoding for zinc

transport, sodium/glucose cotransport, sodium-dependent phosphate transport and

organic anion transport (Slc39a10, Slc5a12, Slc20a2 and Slco2a1 respectively).

Although the involvement of water channels (aquaporins) in cell migration, fat

metabolism, epidermal biology and neural signal transduction point to a role in the

pathophysiology of cancers [107], the current study is the first to show BHA-elicited

regulation via Nrf2 of aquaporins 1, 3 and 8 which may play a role in the overall cancer

chemopreventive effects of BHA. Taken together, the current study suggests that BHA

could coordinately regulate the Phase I, II, and III xenobiotic metabolizing enzyme

genes as well as antioxidative stress genes through Nrf2-dependent pathways in vivo.

The regulation of these genes could have significant effects on prevention of tumor

initiation by enhancing the cellular defense system, preventing the activation of

procarcinogens/reactive intermediates, and increasing the excretion/efflux of reactive

carcinogens or metabolites [56].

Bone morphogenetic proteins (BMPs) are multifunctional signaling molecules

regulating growth, differentiation, and apoptosis in various target cells [108,109]. BMP

receptor 1A (Bmpr1a) is a serine/threonine kinase receptor that mediates the osteogenic

effects of the BMPs and is co-ordinately regulated with transforming growth factor,

beta (Tgfβ) in vitro [110]. Here, we show that Bmpr1a was upregulated along with a

strong induction of Tgfβ (>14-fold) in an Nrf2-dependent manner in small intestine.

Therefore, the current study is the first to identify a role for Nrf2 in co-regulation of

BMP and Tgfβ pathways in BHA-elicited chemopreventive effects in vivo which may

52

be, in part, due to induction of apoptosis caused by activation of BMP [108].

Interestingly, Bmp6 was suppressed in the liver suggesting a spatial regulation of BHA-

regulated chemoprevention via Nrf2 in the liver and small intestine. A recent study

[111] reported that BMP-2 modulates the expression of molecules involved in Wnt

signaling, and activates the canonical Wnt pathway in normal human keratinocytes. In

our study, we also observed an Nrf2-dependent upregulation of wingless-type MMTV

integration site 9B (Wnt9b) along with a down-regulation of skin hornerin (Hrnr) in

small intestine, and an upregulation (greater than 5-fold) of stratum corneum

(epidermis) development genes such as keratin associated protein 6-1 (Krtap6-1) and

keratin complex 2, basic, gene 18 (Krt2-18) in liver. Since BMP-2 and Wnt are

involved in the development of skin and skin appendages [111] as morphogens, and

since Nrf2 has been implicated in hyperproliferation of keratinocytes [112], our results

are the first identification of a putative in vivo cross-talk regulated by Nrf2 and

modulated by BHA between BMP and Wnt pathway members in the

etiopathophysiology and chemoprevention of skin cancers. Further studies in an

appropriate in vivo model as well as in vitro mechanistic studies will be necessary to

enhance our current understanding of regulation of skin cancer by Nrf2.

We have demonstrated previously [90] in vitro that BHA is capable of activating

distinct mitogen-activated protein kinases (MAPKs) including extracellular signal-

regulated protein kinase 2 (ERK2), and c-Jun N-terminal kinase 1 (JNK1). The current

study elucidates the Nrf2-dependent, BHA-modulated regulation in vivo of many

members of the MAPK family including Mapk6, Mapk8, Map3k9, Map4k4, Map4k5,

Map2k7, Mapkap1, and Mapkapk5; as well as the Jun oncogene, thus, validating the

53

physiological relevance of our results. BHA could also alter the expression of many

important signaling biomolecules in discrete signal transduction pathways in an Nrf2-

dependent manner including those of the JAK/STAT pathway (Janus kinase 2, Jak2,

and protein inhibitor of activated Stat4, Pias4). In mammalian cells, insulin-induced

PI3K (phosphoinositide 3-kinase) activation, generates the lipid second messenger

PtdIns (P3), which is thought [113] to play a key role in triggering the activation of p70

ribosomal S6 protein kinase (S6K). The identification in the current study of

phosphoinositide-3-kinase, regulatory subunit 5, p101 (Pik3r5), insulin-like growth

factor 1 (Igf1), Insulin-like growth factor 2 receptor (Igf2r), and Ribosomal protein S6

kinase, polypeptide 5 (Rps6ka5) as Nrf2-dependent and BHA-regulated genes is

interesting as this is the first identification of Igf2r as a target of BHA in vivo, and may

be another putative mechanism by which BHA elicits its Nrf2-mediated

chemopreventive effects. We also observed a BHA-elicited, Nrf2-dependent stimulation

of Diacylglycerolkinase kappa, and epsilon and eta isoforms of protein kinase C (Prkce

and Prkch respectively) which is consistent with reports of PKC-activation by BHA and

diacylglycerol [114,115]. In addition, G protein-coupled receptor kinase 5 (GRK5) and

glycogen synthase kinase 3 beta (Gsk3β), which were down-regulated in small intestine

in an Nrf2-dependent manner, were identified for the first time as putative targets for

BHA-mediated chemoprevention.

BHA could also modulate the expression of many genes involved in apoptosis and cell

cycle control in an Nrf2-dependent manner including cyclin G1 (Ccng1), cyclin T2

(Ccnt2), cyclin I (Ccni), G0/G1 switch gene 2 (G0s2), growth arrest and DNA-damage-

inducible 45 alpha and beta (Gadd45a, Gadd45b), CASP8 and FADD-like apoptosis

54

regulator (Cflar), growth arrest specific 1 (Gas1), G1 to S phase transition 1 (Gspt1),

breast cancer 1 (Brca1), and p21 (CDKN1A)-activated kinase 3 (Pak3). Several other

important categories of genes were identified as Nrf2-dependent and BHA-regulated

such as cell adhesion, biosynthesis and metabolism, ubiquitination and proteolysis,

RNA/protein processing and nuclear assembly, cell growth and differentiation, DNA

replication and G-protein coupled receptors. The current study, thus, addresses the

spatial regulation in mouse small intestine and liver of global gene expression profiles

elicited by BHA in exerting its chemopreventive effects via Nrf2. Since a greater

number of genes in this study were altered in the small intestine as compared to the

liver, and since the phenolic compound BHA (Fig.1) is a lipophilic molecule with a

KowWin (http://www.syrres.com/Esc/est_kowdemo.htm) estimated log octanol/water

partitition coefficient (log P) as high as 3.50, the spatial regulation of gene expression

profiles may be attributed to a complex of physiological factors including partitioning

across the gastrointestinal tract, intestinal transit time, uptake into the hepatobiliary

circulation, exposure parameters such as Cmax, Tmax and AUC, and pharmacokinetics

of disposition after oral administration of BHA. Further studies will be necessary to

address the effect(s) of temporal dependence on pharmacokinetic parameters and gene

expression profiles to further enhance our current understanding of BHA-mediated

chemoprevention mechanisms.

In conclusion, our microarray expression profiling study provides some novel insights

into the pharmacogenomics and spatial regulation of global gene expression profiles

elicited in the mouse small intestine and liver by BHA in an Nrf2-dependent manner

from a gestalt biological perspective. Amongst these BHA-regulated genes, clusters of

http://www.syrres.com/Esc/est_kowdemo.htm

55

Nrf2-dependent genes were identified by comparing gene expression profiles between

C57BL/6J Nrf2(+/+) and C57BL/6J/Nrf2(–/–) mice. The identification of novel

molecular targets that are regulated by BHA via Nrf2 underscores the ineluctable

importance of the Nrf2/ARE signaling pathway in cancer chemoprevention. This study

clearly extends the current latitude of thought on the molecular mechanisms underlying

BHA’s cancer chemopreventive effects as well as the role(s) of Nrf2 in its biological

functions. Future in vivo and in vitro mechanistic studies exploring the germane

molecular targets or signaling pathways as well as Nrf2-dependent genes related to the

significant functional categories uncovered in the current study would inexorably

extend our current understanding of cancer chemoprevention.


The authors are deeply grateful to Mr. Curtis Krier at the Cancer Institute of New Jersey

(CINJ) Core Expression Array Facility for his expert assistance with the microarray

analyses. The authors are also deeply indebted to Ms. Donna Wilson of the Keck Center

for Collaborative Neuroscience, Rutgers University as well as the staff of the Human

Genetics Institute of New Jersey at Rutgers University for their great expertise and help

with the quantitative real-time PCR analyses. This work was supported in part by NIH

grant R01-CA094828.

56

Figure 2.1. Chemical Structure of Butylated hydroxyanisole (BHA)

57

Figure 2.2. Schematic representation of experimental design; SIT, Small Intestine.

58

Figure 2.3. Regulation of Nrf2-dependent gene expression by BHA in mouse small

intestine and liver. Gene expression patterns were analyzed at 3h after administration

of a 200mg/kg single oral dose of BHA; Nrf2-dependent genes that were either induced

or suppressed over two fold were listed. The positive numbers on the y-axis refer to the

number of genes being induced; the negative numbers on the y-axis refer to the number

of genes being suppressed.

59

r2 =0.89

Microarray Data

0 1 2 3 4 5 6

Qua

ntita

tive

Rea

l-tim

e PC

R D

ata

-2

0

2

4

6

8

Figure 2.4. Correlation of microarray data with quantitative real-time PCR data.

Fold changes in gene expression measured by quantitative real-time PCR for each

sample in triplicate (n=3) were plotted against corresponding fold changes from

microarray data (coefficient of determination, r2 = 0.89).

60

CHAPTER 3

Toxicogenomics of Endoplasmic Reticulum stress inducer Tunicamycin in the

Small Intestine and Liver of Nrf2 Knockout and C57BL/6J Mice7.8.9

3.1. Abstract

This objective of this study was to investigate the toxicogenomics and the spatial

regulation of global gene expression profiles elicited by Endoplasmic Reticulum (ER)

stress inducer Tunicamycin (TM) in mouse small intestine and liver as well as to

identify TM-modulated Nuclear Factor-E2-related factor 2 (Nrf2)–dependent genes.

Gene expression profiles were analyzed using 45,000 Affymetrix mouse genome 430

2.0 array and GeneSpring 7.2 software. Microarray results were validated by

quantitative real-time reverse transcription-PCR analyses. Clusters of genes that were

either induced or suppressed more than two fold by TM treatment compared with

vehicle in C57BL/6J/Nrf2(–/–; knockout)and C57BL/6J Nrf2 (+/+; wildtype) mice

genotypes were identified. Amongst these, in small intestine and liver, 1291 and 750

genes respectively were identified as Nrf2-dependent and upregulated, and 1370 and

7Work described in this chapter has been published as Nair, S., Kong, A.-N. T., Toxicol Lett. 2007 Jan 10;168(1):21-39. 8Keywords : Tunicamycin, endoplasmic reticulum stress, Nuclear Factor-E2-related Factor 2, Microarray, Global Gene Expression Profiles. 9Abbreviations : TM, Tunicamycin; Nrf2, Nuclear Factor-E2-related factor 2; ER, Endoplasmic Reticulum; UPR, Unfolded Protein Response; Mapk, Mitogen-activated protein kinase; ARE, Antioxidant response element.

61

943 genes respectively as Nrf2-dependent and downregulated. Based on their biological

functions, these genes can be categorized into molecular chaperones and heat shock

proteins, ubiquitination/proteolysis, apoptosis/cell cycle, electron transport,

detoxification, cell growth/differentiation, signaling molecules/interacting partners,

kinases and phosphatases, transport, biosynthesis/metabolism, nuclear assembly and

processing, and genes related to calcium and glucose homeostasis. Phase II

detoxification/antioxidant genes as well as putative interacting partners of Nrf2 such as

nuclear corepressors and coactivators, were also identified as Nrf2-dependent genes.

The identification of TM-regulated and Nrf2-dependent genes in the unfolded protein

response to ER stress not only provides potential novel insights into the gestalt

biological effects of TM on the toxicogenomics and spatial regulation of global gene

expression profiles in cancer pharmacology and toxicology, but also points to the

pivotal role of Nrf2 in these biological processes.

3.2. Introduction

The endoplasmic reticulum (ER) is an important organelle in which newly synthesized

secretory and membrane-associated proteins destined to the extracellular space, plasma

membrane, and the exo/endocytic compartments are correctly folded and assembled

[116,117]. An imbalance between the cellular demand for protein synthesis and the

capacity of the ER in promoting protein maturation and transport can lead to an

accumulation of unfolded or malfolded proteins in the ER lumen. This condition has

been designated “ER stress” [117,118]. Interestingly, the accumulation of misfolded

protein in the ER triggers an adaptive stress response – termed the unfolded protein

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response (UPR) – mediated by the ER transmembrane protein kinase and

endoribonuclease inositol-requiring enzyme-1α (IRE1α) [68]. The glucosamine-

containing nucleoside antibiotic, Tunicamycin (TM, Figure 3.1), produced by genus

Streptomyces, is an inhibitor of N-linked glycosylation and the formation of N-

glycosidic protein-carbohydrate linkages [119]. It specifically inhibits dolichol

pyrophosphate-mediated glycosylation of asparaginyl residues of glycoproteins [120]

and induces “ER stress”.



factor 2 that has been extensively studied by many research groups cited above as well

as this laboratory [1,40-42]. Under homeostatic conditions, Nrf2 is mainly sequestered

in the cytoplasm by a cytoskeleton-binding protein called Kelch-like erythroid CNC

homologue (ECH)-associated protein 1 (Keap1) [13,40,45]. When challenged with

oxidative stress, Nrf2 is quickly released from Keap1 retention and translocates to the

nucleus [40,49]. We have recently identified [40] a canonical redox-insensitive nuclear

export signal (NES) (537LKKQLSTLYL546) located in the leucine zipper (ZIP) domain

of the Nrf2 protein as well as a redox-sensitive NES (173LLSI-PELQCLNI186) in the

transactivation (TA) domain of Nrf2 [121]. Once in the nucleus, Nrf2 not only binds to

the specific consensus cis-element called antioxidant response element (ARE) present in

the promoter region of many cytoprotective genes [41,43,45], but also to other trans-

acting factors such as small Maf (MafG and MafK) [51] that can coordinately regulate

gene transcription with Nrf2. We have previously reported [41] that different segments

of Nrf2 transactivation domain have different transactivation potential; and that

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different MAPKs have differential effects on Nrf2 transcriptional activity, with ERK

and JNK pathways playing an unequivocal role in positive regulation of Nrf2

transactivation domain activity. To better understand the biological basis of signaling

through Nrf2, it has also become imperative to identify possible interacting partners of

Nrf2 such as coactivators or corepressors apart from trans-acting factors such as small

Maf .

Recently, it was reported [122] that Nrf1, another member of the Cap’ n’ Collar (CNC)

family of basic leucine zipper proteins that is structurally similar to Nrf2, is normally

targeted to the ER membrane, and that ER stress induced by TM in vitro may play a

role in modulating Nrf1 function as a transcriptional activator. We sought to investigate

the potential role of ER stress in modulating Nrf2 function as a transcriptional activator

in vivo. Nrf2 knockout mice are greatly predisposed to chemical-induced DNA damage

and exhibit higher susceptibility towards cancer development in several models of

chemical carcinogenesis [43]. In the present study, we have investigated, by microarray

expression profiling, the global gene expression profiles elicited by oral administration

of TM in small intestine and liver of Nrf2 knockout (C57BL/6J/Nrf2-/-) and wild type

(C57BL/6J) mice to enhance our understanding of TM-regulated toxicological effects

mediated through Nrf2. We have identified clusters of TM-modulated genes that are

Nrf2-dependent in small intestine and liver and categorized them based on their

biological functions. The identification of TM-regulated Nrf2-dependent genes will

yield valuable insights into the role of Nrf2 in TM-modulated gene regulation with

respect to cancer pharmacology and toxicology. This study also enables the

identification of novel molecular targets that are regulated by TM via Nrf2. The current

64

study is also the first to investigate the global gene expression profiles elicited by TM in

an in vivo murine model where the role of Nrf2 is also examined.




knockout mice Nrf2 (-/-) (C57BL/SV129) have been described previously.[100]. Nrf2 (-








products. Female C57BL/6J/Nrf2(-/-) mice from third generation of backcrossing were

used in this study. Age-matched female C57BL/6J mice were purchased from The

Jackson Laboratory (Bar Harbor, ME). Mice in the age-group of 9-12 weeks were

housed at Rutgers Animal Facility with free access to water and food under 12 h

light/dark cycles. After one week of acclimatization, the mice were put on AIN-76A

diet (Research Diets Inc. NJ USA) for another week. The mice were then administered

TM (Sigma-Aldrich, St.Louis, MO) at a dose of 2 mg/kg (dissolved in 50% PEG 400

aqueous solution) by oral gavage. The control group animals were administered only

vehicle (50% PEG 400 aqueous solution). Each treatment was administered to a group

65

of four animals for both C57BL/6J and C57BL/6J/Nrf2(-/-) mice. Mice were sacrificed

3h after TM treatment or 3 h after vehicle treatment (control group). Livers and small

intestines were retrieved and stored in RNA Later (Ambion, Austin,TX) solution.

Sample Preparation for Microarray Analyses : Total RNA from liver and

small intestine tissues were isolated by using a method of TRIzol (Invitrogen, Carlsbad,

CA) extraction coupled with the RNeasy kit from Qiagen (Valencia, CA). Briefly,

tissues were homogenized in trizol and then extracted with chloroform by vortexing. A

small volume (1.2 ml) of aqueous phase after chloroform extraction and centrifugation

was adjusted to 35% ethanol and loaded onto an RNeasy column. The column was

washed, and RNA was eluted following the manufacturer’s recommendations. RNA

integrity was examined by electrophoresis, and concentrations were determined by UV

spectrophotometry.

Microarray Hybridization and Data Analysis : Affymetrix (Affymetrix,

Santa Clara, CA) mouse genome 430 2.0 array was used to probe the global gene

expression profiles in mice following TM treatment. The mouse genome 430 2.0 Array

is a high-density oligonucleotide array comprised of over 45,101 probe sets representing

over 34,000 well-substantiated mouse genes. The library file for the above-mentioned

oligonucleotide array is readily available at http://www.affymetrix.com/support/technical

/libraryfilesmain.affx. After RNAisolation, all the subsequent technical procedures

including quality control and concentration measurement of RNA, cDNA synthesis and

biotin-labeling of cRNA, hybridization and scanning of the arrays, were performed at

66

CINJ Core Expression Array Facility of Robert Wood Johnson Medical School (New

Brunswick, NJ). Each chip was hybridized with cRNA derived from a pooled total

RNA sample from four mice per treatment group, per organ, and per genotype (a total

of eight chips were used in this study) (Fig.2). Briefly, double-stranded cDNA was

synthesized from 5 µg of total RNA and labeled using the ENZO BioArray RNA

transcript labeling kit (Enzo Life Sciences, Inc.,Farmingdale, NY, USA) to generate

biotinylated cRNA. Biotin-labeled cRNA was purified and fragmented randomly

according to Affymetrix’s protocol. Two hundred microliters of sample cocktail

containing 15 μg of fragmented and biotin-labeled cRNA was loaded onto each chip.

Chips were hybridized at 45°C for 16 h and washed with fluidics protocol EukGE-

WS2v5 according to Affymetrix’s recommendation. At the completion of the fluidics

protocol, the chips were placed into the Affymetrix GeneChip Scanner where the

intensity of the fluorescence for each feature was measured. The expression value

(average difference) for each gene was determined by calculating the average of

differences in intensity (perfect match intensity minus mismatch intensity) between its

probe pairs. The expression analysis file created from each sample (chip) was imported

into GeneSpring 7.2 (Agilent Technologies, Inc., Palo Alto, CA) for further data

characterization. Briefly, a new experiment was generated after importing data from the

same organ in which data was normalized by array to the 50th percentile of all

measurements on that array. Data filtration based on flags present in at least one of the

samples was first performed, and a corresponding gene list based on those flags was

generated. Lists of genes that were either induced or suppressed more than two fold

between treated versus vehicle group of same genotype were created by filtration-on-

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fold function within the presented flag list. By use of color-by-Venn-Diagram function,

lists of genes that were regulated more than two fold only in C57BL/6J mice in both

liver and small intestine were created. Similarly, lists of gene that were regulated over

two fold regardless of genotype were also generated.

Quantitative Real-time PCR for Microarray Data Validation : To validate

the microarray data, several genes of interest were selected from various categories for

quantitative real-time PCR analyses. Glyceraldehyde-3-phosphate-dehydrogenase

(GAPDH) served as the “housekeeping” gene. The specific primers for these genes

listed in Table 3.1 were designed by using Primer Express 2.0 software (Applied


Coralville, I A. The specificity of the primers was examined by a National Center for

Biotechnology Information Blast search of the mouse genome. Instead of using pooled

RNA from each group, RNA samples isolated from individual mice as described earlier

were used in real-time PCR analyses. For the real-time PCR assays, briefly, first-strand









68







verified by first-derivative melting curve analysis using the ABI software; and the


The gene expression was determined by normalization with control gene GAPDH. In

order to validate the results, the correlation between corresponding microarray data and

real-time PCR data was evaluated by the statistical ‘coefficient of determination’,

r2=0.97.

3.4. Results

3.4.1. TM-Modulated Gene Expression Patterns in Mouse Small Intestine and

Liver

Subsequent to data normalization, 48.76% (21,991) of the probes passed the filtration

based on flags present in at least one of four small intestine sample arrays depicted in

Figure 3.2. Expression levels of 1291 probes were elevated or of 1370 probes were

suppressed over two fold by TM only in the wild-type mice, while 3471 probes were

induced or 2024 probes were inhibited over two fold by TM only in the Nrf2(–/–) mice

small intestine (Figure 3.3a). Similarly, changes in gene expression profiles were also

observed in mice liver. Overall, the expression levels of 51.495% (23,225) probes were

69

detected in least in one of four liver sample arrays depicted in Figure 3.2. In comparison

with the results from small intestine sample arrays, a smaller proportion of well-defined

genes were either elevated (750) or suppressed (943) over two fold by TM in wild-type

mice liver alone; whereas 39 well-defined genes were induced or 3170 genes were

inhibited in Nrf2(–/–) mice liver. (Figure 3.3b).

3.4.2. Quantitative Real–Time PCR Validation of Microarray Data

To validate the data generated from the microarray studies, several genes from different

categories (Table 3.1) were selected to confirm the TM-regulative effects by the use of

quantitative real-time PCR analyses as described in detail under Materials and Methods.

After ascertaining the amplicon specificity by first-derivative melting curve analysis,

the values obtained for each gene were normalized by the values of corresponding

GAPDH expression levels. The fold changes in expression levels of treated samples

over control samples were computed by assigning unit value to the control (vehicle)

samples. Computation of the correlation statistic showed that the data generated from

the microarray analyses are well-correlated with the results obtained from quantitative

real-time PCR (coefficient of determination, r2 = 0.97, Figure 3.4).

3.4.3. TM-Induced Nrf2-Dependent Genes in Small Intestine and Liver

Genes that were induced only in wild-type mice, but not in Nrf2(–/–) mice, by TM were

designated as TM-induced Nrf2-dependent genes. Based on their biological functions,

these genes were classified into categories, including ubiquitination and proteolysis,

electron transport, chaperones and unfolded protein response genes, detoxification

70

enzymes, transport, apoptosis and cell cycle control, cell adhesion, kinases and

phosphatases, transcription factors and interacting partners, glucose-related genes, ER

and Golgi-related genes, translation factors, RNA/Protein processing and nuclear

assembly, biosynthesis and metabolism, cell growth and differentiation, and G protein-

coupled receptors (Table 3.2 lists genes relevant to our interest).

In response to TM-induced ER stress, several unfolded protein response genes were

identified as Nrf2-regulated including, amongst others, heat shock protein, alpha-

crystallin-related, B6 (Hspb6) in liver, heat shock protein family, member

7,cardiovascular (Hspb7) in small intestine, and stress 70 protein chaperone,

microsome-associated, human homolog (Stch) in both liver and small intestine. A large

number of apoptosis and cell-cycle related genes were also upregulated in response to

TM treatment. Representative members included B-cell leukemia/lymphoma 2 (Bcl2),

CASP8 and FADD-like apoptosis regulator (Cflar), Epiregulin (Ereg), Growth arrest

specific 2 (Gas2) and synovial apoptosis inhibitor 1, synoviolin (Syvn1). Interestingly,

several important transcription/translation factors and interacting partners were

identified as Nrf2-dependent and TM-regulated. These included P300/CBP-associated

factor (Pcaf), Smad nuclear interacting protein 1 (Snip1), nuclear receptor coactivator 5

(Ncoa5), nuclear receptor interacting protein 1 (Nrip1), nuclear transcription factor, X-

box binding-like 1 (Nfxl1), eukaryotic translation initiation factors 1α 2, 4e and 5 (Eif

1a2, 4e and 5), Erbb2 interacting protein (Erbb2ip), cAMP responsive element binding

protein 3-like 2 (Creb3l2) and Jun oncogene (Jun).

Other categories of genes induced by TM in an Nrf2-dependent manner included cell

adhesion (cadherins 1, 2, and 10), glucose-related genes (hexokinase 2), transport

71

(solute carrier family members Slc13a1, Slc22a3, Slc8a1 and others), and ubiquitination

and proteolysis (Constitutive photomorphogenic protein and carboxypeptidase A4). The

glutathione peroxidase 3 (Gpx3) gene was also upregulated in liver in an Nrf2-

dependent manner in response to TM treatment.

3.4.4. TM-Suppressed Nrf2-Dependent Genes in Small Intestine and Liver

As shown in Table 3.3 which lists genes relevant to our interest, TM treatment also

inhibited the expression of many genes falling into similar functional categories in an

Nrf2-dependent manner. Major Phase II detoxifying genes identified as Nrf2-regulated

and TM-modulated included several isoforms of Glutathione-S-transferase (Gst), and

glutamate cysteine ligase, modifier subunit (Gclm). Additionally, Phase I genes such as

cytochrome P450 family members Cyp3a44, Cyp39a1 and Cyp8b1 were also

downregulated in response to TM-treatment in an Nrf2-dependent manner. Moreover,

many transport genes, which may be regarded as Phase III genes, including members of

solute carrier family (Slc23a2, Slc23a1, Slc37a4, Slc4a4, Slc40a1, Slc9a3) and

multidrug-resistance associated proteins (Abcc3) were also downregulated via Nrf2 and

regulated through TM. Thus, a co-ordinated response involving Phase I, II and III genes

was observed on TM treatment in an Nrf2-dependent manner.

Other categories of genes affected included apoptosis and cell cycle-related genes

(Caspases 6 and 11, growth arrest and DNA-damage-inducible 45 β), electron transport

(Cyp450 members and NADH dehydrogenase isoforms), kinases and phosphatases

(mitogen activated protein kinase family members, ribosomal protein S6 kinase),

transcription factors and interacting partners (inhibitor of kappa B kinase gamma and

72

src family associated phosphoprotein 2), and glucose-related genes (glucose-6-

phosphatase, catalytic, fructose bisphosphatase 1, and glucose phosphate isomerase 1).

Superoxide dismutase (Sod1) was also identified as an Nrf2-regulated and TM-

modulated gene that was suppressed. Furthermore, cell adhesion genes (cadherin 22),

ubiquitination and proteolysis genes (Usp25 and Usp34), and some unfolded protein

response genes (heat shock proteins 1B and 3) were also observed to be downregulated

in response to TM treatment via Nrf2.

3.5. Discussion

The major goal of this study was to identify toxicant Tunicamycin-regulated Nrf2-

dependent genes in mice liver and small intestine by using C57BL/6J Nrf2 (+/+;

wildtype) and C57BL/6J/Nrf2(–/–; knockout) mice and genome-scale microarray

analyses. We sought to investigate by transcriptome expression profiling the potential

role of ER stress stimulus in modulating Nrf2 function as a transcriptional activator in

vivo. As a protein-folding compartment, the ER is exquisitely sensitive to alterations in

homeostasis, and provides stringent quality control systems to ensure that only correctly

folded proteins transit to the Golgi and unfolded or misfolded proteins are retained and

ultimately degraded. A number of biochemical and physiological stimuli, such as

perturbation in calcium homeostasis or redox status, elevated secretory protein

synthesis, expression of misfolded proteins, sugar/glucose deprivation, altered

glycosylation, and overloading of cholesterol can disrupt ER homeostasis, impose stress

to the ER, and subsequently lead to accumulation of unfolded or misfolded proteins in

the ER lumen [67]. The ER has evolved highly specific signaling pathways called the

73

unfolded protein response (UPR) to cope with the accumulation of unfolded or

misfolded proteins [67,68]. ER stress stimulus by Thapsigargin has also been shown

[69] to activate the c-Jun N-terminal kinase (JNK) or stress-activated protein kinase

(SAPK) that is a member of the mitogen-activated protein kinase (MAPK) cascade [70].

Moreover, it has been reported that the coupling of ER stress to JNK activation involves

transmembrane protein kinase IRE1 by binding to an adaptor protein TRAF2, and that

IRE1 / fibroblasts were impaired in JNK activation by ER stress [71]. We have

previously reported that phenethyl isothiocyanate (PEITC) from cruciferous vegetables

activates JNK1 [72] and that the activation of the antioxidant response element (ARE)

by PEITC involves both Nrf2 and JNK1 [42] in HeLa cells. We have also reported [41]

that extracellular signal-regulated kinase (ERK) and JNK pathways play an unequivocal

role in positive regulation of Nrf2 transactivation domain activity in vitro in HepG2

cells. Recently, it was shown [122] that Nrf1, another member of the Cap’ n’ Collar

(CNC) family of basic leucine zipper proteins that is structurally similar to Nrf2, is

normally targeted to the ER membrane, and that ER stress induced by TM in vitro may

play a role in modulating Nrf1 function as a transcriptional activator. Here, we

investigated the role of Nrf2 in modulating transcriptional response to ER stress

stimulus by TM in vivo in an Nrf2 (-/-; deficient) murine model, thus providing new

biological insights into the diverse cellular and physiological processes that may be

regulated by the UPR in cancer pharmacology and toxicology.

Interestingly, a co-ordinated response involving Phase I, II and III genes that has not

been demonstrated earlier was observed in vivo on ER stress induction with TM in an

Nrf2-dependent manner. Phase I drug-metabolizing enzymes (DMEs) such as

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cytochrome P450 family members Cyp3a44, Cyp39a1 and Cyp8b1 were downregulated

in response to TM-treatment in an Nrf2-dependent manner. Additionally, major Phase II

detoxifying genes identified as Nrf2-regulated and TM-modulated included several

isoforms of Glutathione-S-transferase (Gst), and glutamate cysteine ligase, modifier

subunit (Gclm). Moreover, many transport genes, which may be regarded as Phase III

genes, including members of solute carrier family (Slc23a2, Slc23a1, Slc37a4, Slc4a4,

Slc40a1, Slc9a3) and multidrug-resistance associated proteins (Abcc1, Abcc3 and

Mdr1b or Abcb1b) were also downregulated via Nrf2 and regulated through TM. The

co-ordinated regulation of these genes could have significant effects in toxicology by

enhancing the cellular defense system, preventing the activation of

procarcinogens/reactive intermediates, and increasing the excretion/efflux of reactive

carcinogens or metabolites.

There could be two possible outcomes of prolonged ER stress: (1) an adaptive response

promoting cell survival; or (2) the induction of apoptotic cell death [118]. Indeed,

several genes related to apoptosis and cell cycle control were modulated in response to

TM stimulus in vivo in an Nrf2-dependent manner. The major genes upregulated in this

category included the anti-apoptotic B-cell leukemia/lymphoma 2 (Bcl2) family gene,

CASP8 and FADD-like apoptosis regulator (Cflar), Epiregulin (Ereg), Growth arrest

specific 2 (Gas2), cyclin T2 (Ccnt2) and cyclin-dependent kinase 7 (Cdk7) all in small

intestine apart from mucin 20 (Muc20) and synovial apoptosis inhibitor 1, synoviolin

(Syvn1) in liver ; whereas genes downregulated in this category included cyclin-

dependent kinase 6 (Cdk6) and Bcl2 in liver, baculoviral inhibitor of apoptosis (IAP)-

repeat containing 6 (Birc6) and Caspases 6 and 11 in small intestine, and growth arrest

75

and DNA-damage-inducible 45 – β (Gadd45b), and gamma interacting protein 1

(Gadd45gip1) - in liver and small intestine respectively amongst others. To our

knowledge, this is the first report in vivo of apoptosis and cell cycle-related genes that

are both modulated by the ER stress inducer TM and regulated via Nrf2. Moreover, it

has been noted [123] that although the basic machinery to carry out apoptosis appears to

be present in essentially all mammalian cells at all times, the activation of the suicide

program is regulated by many different signals that originate from both the intracellular

and the extracellular milieu. Notably, transcription factor NF-κB is critical for

determining cellular sensitivity to apoptotic stimuli by regulating both mitochondrial

and death receptor apoptotic pathways. Recently, it was reported [57] that autocrine

tumor necrosis factor alpha links ER stress to the membrane death receptor pathway

through IRE1alpha-mediated NF-κB activation and down-regulation of TRAF2

expression. In our study, we saw a downregulation of inhibitor of kappaB kinase

gamma (Iκbkg or IKKγ) in liver in an Nrf2-dependent manner in response to TM-

induced ER stress. Since the catalytic subunits, IKK and IKKß, require association

with the regulatory IKKγ (NEMO) component to gain full basal and inducible kinase

activity and since tetrameric oligomerization of IκB Kinase γ (IKKγ) is obligatory for

IKK Complex activity and NF-κB activation [124], our results appear to be validated

from a functional standpoint and underscore the complexity of factors involved in

making the decision between cell survival and cell death in response to TM-mediated

ER stress in vivo, not excluding the possibility of potential cross-talk between

Nrf2/ARE pathway and other signaling pathways that may converge at multiple levels

in the cell.

76

Interestingly, impaired proteasome function through pharmacological inhibition, or by

accumulation of malfolded protein in the cytoplasm, can ultimately block ER-associated

degradation (ERAD) [125] which is important for eviction of malfolded proteins from

the ER to the cytoplasm where they are subsequently ubiquitinated and degraded via the

proteasome. In our study, several genes associated with the ubiquitin/proteasome

pathway were regulated in response to TM in an Nrf2-dependent manner. These

included, amongst others, constitutive photomorphogenic protein (Cop1),

carboxypeptidase A4 (Cpa4), ubiquitin-specific peptidase 34 (Usp34), and ubiquitin-

specific processing protease (Usp25). Furthermore, UPR genes such as various heat

shock proteins (Hspb3, Hspb6, Hspb7, Hspa1B) and molecular chaperones and folding

enzymes, e.g., stress 70 protein chaperone (Stch) were also seen to be regulated by TM-

induced ER stress and modulated by Nrf2. Since the UPR directs gene expression

important for remediating accumulation of malfolded protein in the ER, the

identification of UPR-responsive genes in our study validates our results from a

biological perspective. Moreover, important genes related to glycosylation

modifications (e.g., galactosyltransferase, B3galt1), ER to Golgi transport (ADP-

ribosylation factor GTPase activating protein 3, Arfgap3; coatomer protein complex

subunit alpha, Copa; Lectin, mannose-binding 1,Lman1), and intra-Golgi transport

(Golgi associated, gamma adaptin ear containing, ARF binding protein 2, Gga2) were

also seen to be regulated by TM in an Nrf2-dependent manner. Genes related to

biogenesis of ribosomes on rough ER where proteins are synthesized from mRNA, e.g.,

brix domain containing 2 (Bxdc2) and ribosomal protein S6 kinase, polypeptides 1

(Rps6ka1) and 4 (Rps6ka4), were also regulated via Nrf2 and modulated by TM

77

treatment. To our knowledge, this is the first in vivo investigation examining the

potential role of Nrf2 and TM-induced ER stress in the simultaneous modulation of

UPR-responsive genes, clearance by the ubiquitin/proteasome pathway members, and

cellular biosynthetic-secretory pathway involving ribosomal biogenesis genes and ER to

Golgi transport genes.

Additionally, many genes related to glucose biosynthesis and metabolism including

glucose phosphate isomerase 1 (gluconeogenesis/glycolysis), fructose bisphosphatase

1(gluconeogenesis), glucose-6-phosphatase (glycogen biosynthesis), hexokinase 2

(glycolysis), adiponectin (glucose metabolism), lectins (galactose- and mannose-

binding) and the solute carrier family member Slc 35b1 (sugar porter) were all seen to

be regulated through Nrf2 and modulated by TM-induced ER stress. The simultaneous

modulation of genes encoding for insulin like growth factor receptors 1 and 2 point to a

potential role for glucose- and ER stress-mediated insulin resistance [126] wherein the

potential role of Nrf2 has never been examined earlier.

In recent times, there is a renewed interest in dissecting the interacting partners of Nrf2

such as coactivators and corepressors which are co-regulated with Nrf2 to better

understand the biochemistry of Nrf2. In a recent microarray study [55], we have

reported that CREB-binding protein (CBP) was upregulated in mice liver on treatment

with (-)epigallocatechin-3-gallate (EGCG) in an Nrf2-dependent manner. We have also

demonstrated [41] previously, using a Gal4-Luc reporter co-transfection assay system in

HepG2 cells, that the nuclear transcriptional coactivator CBP, which can bind to Nrf2

transactivation domain and can be activated by extracellular signal-regulated protein

kinase (ERK) cascade, showed synergistic stimulation with Raf on the transactivation

78

activities of both the chimera Gal4-Nrf2 (1-370) and the full-length Nrf2. In the current

study, we observed the upregulation of the P300/CBP-associated factor (P/CAF), trans-

acting factor v-maf musculoaponeurotic fibrosarcoma oncogene family, protein F (Maf

F), nuclear receptor co-activator 5 (Ncoa5), nuclear receptor co-repressor interacting

protein (Nrip1) and Smad nuclear interacting protein 1 (Snip1) ; as well as

downregulation of the src family associated phosphoprotein 2 (Scap2) in an Nrf2-

dependent manner. Although microarray expression profiling cannot provide evidence

of binding between partners, this is the first investigation to potentially suggest that co-

repressor Nrip1 and co-activators P/CAF and Ncoa5, similar to CBP in our previous

studies, may serve as putative TM-regulated nuclear interacting partners of Nrf2 in

eliciting the UPR-responsive events in vivo. We have also shown recently [61] that

coactivator P/CAF could transcriptionally activate a chimeric Gal4-Nrf2-Luciferase

system containing the Nrf2 transactivation domain in HepG2 cells. In addition, P/CAF

which is known [62] to be a histoneacetyl transferase protein has recently been shown

[63] to mediate DNA damage-dependent acetylation on most promoters of genes

involved in the DNA-damage and ER-stress response, which validates our observation

of P/CAF induction via Nrf2 in response to TM-induced ER stress. Taken together, it is

tempting to speculate that the TM-regulated pharmacological and toxicological effects

may be regulated by a multimolecular complex, which involves Nrf2 along with the

transcriptional co-repressor Nrip1 and the transcriptional co-activators P/CAF and

Ncoa5, in addition to the currently known trans-acting factors such as small Maf [51],

with multiple interactions between the members of the putative complex as we have

shown recently with the p160 family of proteins [61]. Indeed, further studies of a

79

biochemical nature would be needed to substantiate this hypothesis and extend our

understanding of Nrf2 regulation in TM-mediated ER stress.

Many important transcription factors affecting diverse signaling pathways were

identified as regulated through Nrf2 and modulated by TM treatment. For example, Jun

oncogene, platelet-derived growth factor, metallothionein 1 and 2, transforming growth

factor beta 1 and ErbB2 interacting protein were upregulated ; whereas hypoxia-

inducible factor 1, alpha subunit inhibitor, peroxisome proliferator activated receptor

binding protein, v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) and

protein kinase C binding protein 1 were downregulated via Nrf2 in response to TM.

Since these transcription factors can modulate the expression of many different gene

transcripts encoding various proteins, their identification as Nrf2-regulated and ER-

stress- or TM-modulated would be important in enhancing our current understanding of

UPR responsive genes and in providing new biological insights into the diverse cellular

and physiological processes that may be regulated by the UPR in Nrf2-regulated cancer

pharmacology and toxicology.

In the category of kinases and phosphatases, several members of the MAPK cascade

such as Map2k7, Mapk14, Mapk8, Map3k7 as well as MAPK-activated protein kinase 5

(Mapkapk5) were identified as regulated by TM via Nrf2. Moreover, members of the

calcium/calmodulin signaling pathway such as calcium/calmodulin-dependent - protein

kinase I gamma (Camkg), -protein kinase 1D (Camk1d) and -protein kinase IV

(Camk4) were shown to be regulated by TM in an Nrf2-dependent manner.

Interestingly, glutathione peroxidase 3 (Gpx3) was upregulated and superoxide

dismutase 1 (Sod1) was downregulated by TM via Nrf2 which can have important

80

implications in oxidative stress-mediated [22] pathophysiology or ER stress caused by

perturbations in redox circuitry [22,67,74]

Indeed, there is a growing interest amongst researchers in targeting the UPR in

cancerous tumor growth [73]. Recently, it was shown [127] that the proteasomal

inhibitor bortezomib induces a unique type of ER stress characterized by an absence of

eif2alpha phosphorylation, ubiquitylated protein accumulation, and proteotoxicity in

human pancreatic cancer cells. It was also reported [128] that malignant B cells may be

highly dependent on ER-Golgi protein transport and that targeting and inhibiting this

process by brefeldin A may be a promising therapeutic strategy for B-cell malignancies,

especially for those that respond poorly to conventional treatments, e.g., fludarabine

resistance in chronic lymphocytic leukemia (CLL). However, the role of Nrf2 in

modulating the UPR in vivo has never been examined before.

The current study, thus, addresses the spatial regulation in mouse small intestine and

liver of global gene expression profiles elicited by TM-mediated ER stress via Nrf2.

Several common clusters of genes such as that for ubiquitin/proteasome, cell adhesion,

transcription factors were observed in this study that were also observed in previous

studies with Nrf2 activators [38,52,55,56,129] which validates our studies from a

functional standpoint. In addition, three clusters of genes – calcium homeostasis,

ER/Golgi transport & ER/Golgi biosynthesis/metabolism genes, and glucose

homeostasis genes – were uniquely observed as modulated via Nrf2 in response to TM-

mediated ER stress that were not discernible in previous studies with Nrf2 activators.

Indeed, the involvement of the three clusters mentioned above is a rational response to

alteration in the homeostatic environment brought about by the toxicant TM-induced

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ER stress, and is reflective of their potential role in the UPR to ER stress that is

naturally not observed in previous studies on cancer chemoprevention with Nrf2

activators that do not induce ER stress. The presence of the three unique clusters as

mentioned above that relate to the putative role of these genes in the UPR is an effect

that appears to be elicited in a toxicant-specific manner. In addition, classical Phase II

genes such as Gst isoforms and Gclm were downregulated in a Nrf2-dependent fashion

in response to the toxicant TM at 3 hours in this study. We were able to see the

downregulation of classical Phase II genes in qRT-PCR experiments performed at a 12

hour time-point (data not shown) with the extent of downregulation being more

pronounced at 12 hours than at 3 hours in response to the toxicant TM. Interestingly,

this contrasts with the delayed response reported for the classical Phase II gene NQO1

in response to Nrf2 activator BHA (Butylated hydroxyanisole) wherein the induction of

the gene peaked at 12 hours [52] with no gene induction at 3 hours. Taken together, the

downregulation of classical Phase II genes in response to TM-induced ER stress should

be viewed in the light of a complex of physiological factors including partitioning

across the gastrointestinal tract, intestinal transit time, uptake into the hepatobiliary

circulation, exposure parameters such as Cmax, Tmax and AUC, and pharmacokinetics

of disposition after oral administration of TM. Further studies will be necessary to

address the effect(s) of temporal dependence on pharmacokinetic parameters and gene

expression profiles to further enhance our current understanding of TM-mediated ER

stress response, the complexity of kinetics of Phase II gene expression response to a

toxicant and the role of Nrf2.

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In conclusion, our microarray expression profiling study provides some novel insights

into the pharmacogenomics and spatial regulation of global gene expression profiles

elicited in the mouse small intestine and liver by TM in an Nrf2-dependent manner from

a biological perspective. Amongst these TM-regulated genes, clusters of Nrf2-

dependent genes were identified by comparing gene expression profiles between

C57BL/6J Nrf2(+/+) and C57BL/6J/Nrf2(–/–) mice. The identification of novel

molecular targets that are regulated by TM via Nrf2 in vivo raises possibilities for

targeting the UPR proteins in future to augment or suppress the ER stress response and

modulate disease progression. This study clearly extends the current latitude of thought

on the molecular mechanisms underlying TM-mediated UPR effects as well as the

role(s) of Nrf2 in its biological functions. Future in vivo and in vitro mechanistic studies

exploring the germane molecular targets or signaling pathways as well as Nrf2-

dependent genes related to the significant functional categories uncovered in the current

study would greatly extend our understanding of the diverse cellular and physiological

processes that may be regulated by the UPR in cancer pharmacology and toxicology,

and the potential role of ER stress in modulating Nrf2 function as a transcriptional

activator.


The authors are deeply grateful to Mr. Curtis Krier at the Cancer Institute of New Jersey

(CINJ) Core Expression Array Facility for his expert assistance with the microarray

analyses. The authors are also deeply indebted to Ms. Donna Wilson of the Keck Center

for Collaborative Neuroscience, Rutgers University as well as the staff of the Human

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Genetics Institute of New Jersey at Rutgers University for their great expertise and help

with the quantitative real-time PCR analyses. This work was supported in part by NIH

grant R01- 094828.

84

Figure 3.1. Chemical Structure of Tunicamycin (TM).

85

Figure 3.2. Schematic representation of experimental design; SIT, Small Intestine.

86

Num

ber o

f Gen

es

-2000

-1500

-1000

-500

0

500

1000

1500

Small Intestine Liver

Figure 3.3. Regulation of Nrf2-dependent gene expression by TM in mouse small

intestine and liver. Gene expression patterns were analyzed at 3h after administration

of a 2mg/kg single oral dose of TM; Nrf2-dependent genes that were either induced or

suppressed over two fold were listed. The positive numbers on the y-axis refer to the

number of genes being induced; the negative numbers on the y-axis refer to the number

of genes being suppressed.

87

Microarray Data

2 4 6 8 10 12 14

Qua

ntita

tive

Rea

l-tim

e PC

R D

ata

0

2

4

6

8

10

12

14

r2=0.97

Figure 3.4. Correlation of microarray data with quantitative real-time PCR data.

Fold changes in gene expression measured by quantitative real-time PCR for each

sample in triplicate (n=3) were plotted against corresponding fold changes from

microarray data (coefficient of determination, r2 = 0.97).

88

CHAPTER 4

Synergistic effects of a combination of dietary factors sulforaphane and

(-) epigallocatechin-3-gallate in HT-29 AP-1 human colon carcinoma cells10,11,12

4.1. Abstract

The objective of this study was to investigate combinations of two chemopreventive

dietary factors: EGCG 20 μM (or 100 μM) and SFN (25 μM) in HT-29 AP-1 human

colon carcinoma cells. After exposure of HT-29 AP-1 cells to SFN and EGCG,

individually or in combination, we performed AP-1 luciferase reporter assays, cell

viability assays, isobologram analyses, senescence staining, quantitative real-time PCR

(qRT-PCR) assays, western blotting, and assays for HDAC activity and hydrogen

peroxide. In some experiments, we exposed cells to superoxide dismutase (SOD) or

Trichostatin A (TSA) in addition to the treatment with dietary factors. The

combinations of SFN and EGCG dramatically enhanced transcriptional activation of

AP-1 reporter in HT-29 cells (46-fold with 25 μM SFN and 20 μM EGCG;

and 175-fold with 25 μM SFN and 100 μM EGCG). Isobologram analysis showed

synergistic activation for the combinations with combination index, CI<1. Interestingly,

10Work described in this chapter has been published as Nair, S., Kong, A.-N. T., Pharm Res. 2007 Jul 27; [Epub ahead of print]. 11Keywords : Isothiocyanate, sulforaphane, EGCG, colon cancer, AP-1, combination. 12Abbreviations : SFN, sulforaphane; EGCG, (-) epigallocatechin-3-gallate; MAPK, mitogen-activated protein kinase; SOD, superoxide dismutase; HDAC, histone deacetylase; TSA, Trichostatin A; qRT-PCR, quantitative real-time PCR; AP-1, activator protein-1.

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co-treatment with 20units/ml of SOD, a free radical scavenger, attenuated the synergism

elicited by the combinations (2-fold with 25 μM SFN and 20 μM EGCG; and 15-fold

with 25 μM SFN and 100 μM EGCG). Cell viability assays showed that the low-dose

combination decreased cell viability to 70% whereas the high-dose combination

decreased cell viability to 40% at 48 hrs, with no significant change in cell viability at

24 hours as compared to control cells. In addition, 20 μM and 100 μM EGCG, but not

25 μM SFN, showed induction of senescence in the HT-29 AP-1 cells subjected to

senescence staining. However, both low- and high-dose combinations of SFN and

EGCG attenuated the cellular senescence induced by EGCG alone. There was no

significant change in the protein levels of phosphorylated forms of ERK, JNK, p38, and

Akt-Ser473 or Akt-Thr308. Besides, qRT-PCR assays corroborated the induction of the

luciferase gene seen with the combinations in the reporter assay. Relative expression

levels of transcripts of many other genes known to be either under the control of the

AP-1 promoter or involved in cell cycle regulation or cellular influx-efflux such as

cyclin D1, cMyc, ATF-2, Elk-1, SRF, CREB5, SLCO1B3, MRP1, MRP2 and MRP3

were also quantified by qRT-PCR in the presence and absence of SOD at both 6hr and

10hr. In addition, pre-treatment with 100ng/ml TSA, a potent HDAC inhibitor,

potentiated (88-fold) the synergism seen with the low-dose combination on the AP-1

reporter transcriptional activation. Cytoplasmic and nuclear fractions of treated cells

were tested for HDAC activity at 2hr and 12 hr both in the presence and absence of

TSA, however, there was no significant change in their HDAC activity. In addition, the

H2O2 produced in the cell system was about 2 μM for the low-dose combination which

was scavenged to about 1 μM in the presence of SOD. Taken together, the synergistic

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activation of AP-1 by the combination of SFN and EGCG that was potentiated by

HDAC inhibitor TSA and attenuated by free radical scavenger SOD point to a possible

multifactorial control of colon carcinoma that may involve a role for HDACs, inhibition

of cellular senescence, and SOD signaling.

4.2. Introduction

Colorectal cancer (cancer of the colon or rectum), according to the Centers for Disease

Control and Prevention (CDC) [130], is the second leading cause of cancer-related

deaths and the third most common cancer in men and in women in the United States. In

addition, the National Cancer Institute (NCI)’s Surveillance Epidemiology and End

Results (SEER) Statistics Fact Sheets [131] show that, based on rates from 2001-2003,

5.56% of men and women born today will be diagnosed with cancer of the colon and

rectum during their lifetime, i.e., 1 in 18 men and women in the United States are at a

lifetime risk of developing colorectal cancer. Since colorectal cancer is initiated in

colonic crypts, a succession of genetic mutations or epigenetic changes can lead to

homeostasis in the crypt being overcome, and subsequent unbounded growth [132].

Using mathematical models of tumorigenesis through failure of programmed cell death

or differentiation, it was predicted [133] that exponential growth in cell numbers does

sometimes occur, usually when stem cells fail to die or differentiate. At other times,

exponential growth does not occur, instead, the number of cells in the population

reaches a new, higher equilibrium which may explain many aspects of tumor behavior

including early premalignant lesions such as cervical intraepithelial neoplasia [133].

The development of colon cancer results from the sequential accumulation of activating

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mutations in oncogenes, such as ras, and inactivating mutations, truncations, or

deletions in the coding sequence of several tumor suppressor genes, including p53 and

adenomatous polyposis coli (APC), together with aberrant activity of molecules

controlling genomic stability [134,135].

Epidemiological studies have revealed an inverse correlation between the intake of

cruciferous vegetables and the risk of certain types of cancer [136,137]. It has also been

reported [138] that because elevated vegetable consumption has been associated with a

lower risk of colorectal cancer, vegetables may have a stronger role in preventing the

progression of adenomas to carcinomas rather than in preventing the initial appearance

of adenomas. Isothiocyanates are a chemical class of compounds that are not naturally

present in cruciferous vegetables, such as broccoli and cauliflower, but are nevertheless

generated from hydrolysis of secondary metabolites known as glucosinolates by the

enzyme myrosinase during the process of vegetable crushing or mastication [139] .

Also, they may be produced in the intestines where resident microflora can promote the

hydrolysis of glucosinolates to isothiocyanates [140]. Sulforaphane (SFN), an

isothiocyanate compound found at high levels in broccoli and broccoli sprouts, is a

potent inducer of phase 2 detoxification enzymes and inhibits tumorigenesis in animal

models [141]. Indeed, sulforaphane has been implicated in a variety of anti-

carcinogenic mechanisms including effects on cell cycle checkpoint controls and cell

survival and/or apoptosis in various cancer cells [141], however, epidemiological

studies indicate [142] that the protective effects in humans may be influenced by

individual genetic variation (polymorphisms) in the metabolism and elimination of

isothiocyanates from the body. Recently, we reported [17] that SFN induces

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hemoxygenase-1 (HO-1) by activating the antioxidant response element (ARE) through

the induction of Nrf2 protein in HepG2 cells and that overexpression of all four p38

mitogen-activated protein kinase (MAPK) isoforms negatively regulated the

constitutive and inducible ARE-dependent gene expression. Myzak et al [141] have also

reported inhibition of histone deacetylase (HDAC) as a novel mechanism of

chemoprotection by SFN.

Green tea polyphenol (-) epigallocatechin-3-gallate (EGCG) is noted to suppress

colonic tumorigenesis in animal models and epidemiological studies. The water-

extractable fraction of green tea contains abundant polyphenolic compounds, in which

EGCG is the major constituent (>50% of polyphenolic fraction) [143]. It has been

reported that EGCG, when administered to rats, inhibited azoxymethane-induced colon

tumorigenesis [144], and also blocked the formation of 1,2-dimethylhydrazine-induced

colonic aberrant crypt foci [145], which is a typical precursor lesion of chemical-

initiated colon cancer. Recently [146], EGCG was reported to inhibit inflammation-

associated angiogenesis by targeting inflammatory cells, mostly neutrophils, and also

inhibit the growth of the highly angiogenic Kaposi's sarcoma tumor cells (KS-Imm) in

nude mice. We have observed [14] that EGCG treatment causes damage to

mitochondria, and that c-jun N-terminal kinase (JNK) mediates EGCG-induced

apoptotic cell death in HT-29 human colon cancer cells. EGCG is also reported

[147,148] to inhibit DNA methyltransferase with demethylation of the CpG islands in

the promoters, and to reactivate methylation-silenced genes such as p16INK4a, retinoic

acid receptor beta, O6-methylguanine methyltransferase, human mutL homolog 1, and

glutathione S-transferase-pi in human colon cancer HT-29 cells, esophageal cancer

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KYSE 150 cells, and prostate cancer PC3 cells. These activities could be enhanced by

the presence of HDAC inhibitors or by a longer-term treatment [148].

Transcription factor activator protein-1 (AP-1) is a redox-sensitive transcription factor

that senses and transduces changes in cellular redox status and modulates gene

expression responses to oxidative and electrophilic stresses presumably via sulfhydryl

modification of critical cysteine residues found on this protein and/or other upstream

redox-sensitive molecular targets [149]. In budding yeast, the transcription factor Yap1

(yeast AP1), which is a basic leucine zipper (bZip) transcription factor, confers the

cellular response to redox stress by controlling the expression of the regulon that

encodes most yeast antioxidant proteins [8]. AP-1 is responsive to low levels of

oxidants resulting in AP-1/DNA binding and an increase in gene expression. AP-1

activation is typically due to the induction of JNK activity by oxidants resulting in the

phosphorylation of serine 63 and serine 73 in the c-Jun transactivation domain

[7,28,29]. With high concentrations of oxidants, AP-1 is inhibited and gene expression

is impeded. Inhibition of AP-1/DNA interactions is attributed to the oxidation of

specific cysteine residues in c-Jun’s DNA binding region, namely cysteine 252 [7,30].

Indeed, for AP-1, a nuclear pathway to reduce the Cys of the DNA-binding domain is

apparently distinct from the upstream redox events that activate the signaling kinase

pathway [6].

The fate of cancer chemopreventive strategies relies largely on the ability to maximally

exploit the intrinsic anti-carcinogenic potential of chemopreventive agents, both singly

and in combination, without incurring undue toxicity. Targeting multiple signal

transduction pathways involved in different stages of carcinogenesis by use of

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combinatorial approaches would ideally empower the clinician to better manage or

delay the progression of the disease. Given the abundance of literature on the

multifarious anti-carcinogenic mechanisms of SFN and EGCG, we investigated the

combinations of these two dietary factors and the role(s) mediated by redox

transcription factor AP-1 in modulating the anti-cancer potential of this putative

chemopreventive combination for the management of colon cancer.


Cell culture and Reagents : Human colon carcinoma HT-29 cells were stably

transfected with an Activator Protein (AP-1) luciferase reporter construct, and are

referred to as HT-29 AP-1 cells. The cells were cultured in Minimum Essential Medium

(MEM) containing 10% Fetal Bovine Serum (FBS) and 1% Penicillin-Streptomycin.

Twelve hours prior to experimental treatments, the cells were exposed to MEM

containing 0.5% FBS. Sulforaphane (SFN) was obtained from LKT Labs, (-)

epigallocatechin-3-gallate (EGCG), superoxide dismutase (SOD) were obtained from

Sigma-Aldrich Co., and Trichostatin A (TSA) was obtained from Biomol. SFN, EGCG

and TSA were dissolved in dimethylsulfoxide (DMSO, Sigma), whereas SOD was

dissolved in 1X phosphate-buffered saline (PBS).

Reporter gene assays : HT-29 AP-1 cells were seeded in six-well culture plates

and treated in duplicate with dimethylsulfoxide (control), 20 μM EGCG, 100 μM

EGCG, 25 μM SFN, 20 μM EGCG + 25 μM SFN, or 100 μM EGCG + 25 μM SFN for

24 hours. Thereafter, the supernatant medium was aspirated on ice, cells were washed

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thrice with ice-cold 1X PBS, treated with 1X Luciferase Reporter Lysis Buffer

(Promega) and subjected to one cycle of snap freeze-thaw at -80 °C. Cell lysates were

harvested with sterile RNAse-free and DNAse-free cell scrapers into microcentrifuge

tubes that were immediately placed on ice. They were then centrifuged at 4 °C for ten

minutes at 13000 x g and returned to ice. Twenty microliters of supernatant solution

was analyzed for relative luciferase activity using a Sirius Luminometer (Berthold

Detection Systems). The relative luciferase activities were normalized by protein

concentrations of individual samples as described below.

Protein Assays : Protein concentrations of samples were determined by the

bicinchonic acid-based BCA Protein Assay Kit (Pierce) according to the manufacturer’s

instructions using a 96-well plate. Standard curves were constructed using bovine serum

albumin (BSA) as a standard. The sample readings were obtained on a μQuant

microplate reader (Bio-tek Instruments, Inc.) at 560nm.

Western blotting : HT-29 AP-1 cells were subjected to treatment with different

dietary factors for one or two hours and harvested on ice with either 1X Whole Cell

Lysis Buffer or 1X MAPK Buffer. Protein (20 μg) was boiled with sample loading

buffer containing β-mercaptoethanol and loaded onto 18-well Criterion Pre-cast gels

(Bio-Rad) with Precision Plus Dual Color Protein Marker (Bio-Rad). Electrophoresis

was performed at 200 V and semi-dry transfer of each gel was effected onto a

polyvinylidine difluoride (PVDF) membrane in an electroblotter at 130mA for 1.5

hours. The membranes were then blocked with 5% BSA in TBST for one hour, washed

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thrice for ten minutes each with 1X TBST, and incubated with primary antibodies

against ERK, JNK, p38, AKT Ser473, AKT Thr308, and Actin (Cell Signaling Inc.) in

3% BSA in TBST (1:2000 antibody dilutions, except beta-actin which was 1:1000) for

one hour at room temperature with gentle agitation. After further three washes with 1X

TBST, the membranes were incubated with appropriate secondary antibodies in 3%

BSA in TBST (1:2000 for P-ERK and P-JNK, 1:5000 for P-p-38 and beta-actin, and

1:10,000 for phosphorylated forms of Akt) overnight at 4 °C with gentle rocking. The

following day, the membranes were washed again thrice with 1X TBST and treated

with ECL chemiluminescence reagent (Pierce) and visualized using a Bio-Rad Imaging

Station. The protein expression was normalized against that of actin as a control.

Cell Viability Assays : The cell viability assays were performed in 24-well cell

culture plates using MTS Assay Kit (Promega) according to the manufacturer’s

instructions. Cell viability was determined at both 24 and 48 hours after treatment with

dietary factors. The absorbance readings were obtained on an μQuant microplate reader

(Bio-tek Instruments, Inc.) at recommended wavelength of 490nm.

RNA Extraction and Assessment of RNA Integrity : HT-29 AP-1 cells were

subjected to treatment with different dietary factors in triplicate for 6hours or 10 hours.

RNA was harvested using the RNeasy Mini Kit (Qiagen) according to the

manufacturer’s instructions. RNA integrity was assessed using formaldehyde gels in 1X

MOPS buffer and RNA concentration was determined by the 260/280 ratio on a DU

530 UV/Visible spectrophotometer (Beckman).

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Quantitative Real-time PCR Assays : Several genes of interest including

luciferase gene as well as genes known to be either under the control of the AP-1

promoter or involved in cell cycle regulation or cellular influx-efflux such as cyclin D1,

cMyc, ATF-2, Elk-1, SRF, CREB5, MDR1, SLCO1B3, MRP1, MRP2 and MRP3 were

selected for quantitative real-time PCR analyses both in the presence or absence of SOD

treatment. Beta-actin served as the “housekeeping” gene. The specific primers for these

genes listed in Table 4.1 were designed by using Primer Express 2.0 software (Applied



Biotechnology Information Blast search of the human genome. For the real-time PCR

assays, briefly, after the RNA extraction and assessment of RNA integrity, first-strand












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The gene expression was determined by normalization with control gene beta-actin.

Hydrogen Peroxide Assays : The levels of hydrogen peroxide in the cell-free

medium was ascertained by the Amplex Red Hydrogen Peroxide Assay Kit (Molecular

Probes/Invitrogen) according to the manufacturer’s instructions. Briefly, a working

solution of 100 μM Amplex Red reagent and 0.2U/ml HRP was prepared, of which 50

μl was added to each microplate well containing the positive control (10 μM H2O2),

negative control (1X Reaction Buffer without H2O2) and test samples. The fluorescence

signal was measured on a FLx-800 microplate fluorescent reader (Bio-tek Instruments,

Inc.) at excitation wavelength of 560 nm and emission wavelength of 590 nm.

HDAC Activity Assays : Cytoplasmic and nuclear fractions of cells treated

with dietary factors, both in the presence and absence of 100ng/ml TSA, were extracted

using the Ne-Per extraction kit (Pierce). The HDAC activity was determined using a

Fluor-de-Lys HDAC Fluorescent Activity Assay Kit (Biomol) according to the

manufacturer’s instructions. Briefly, incubations were performed at 37°C for 10 min

with HeLa nuclear cell extracts containing known HDAC activity that were provided by

the manufacturer. The HDAC reaction was initiated by the addition of Fluor-de-Lys

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substrate. After 10 min, the reaction was quenched by adding the Fluor-de-Lys

Developer, and the mixture was incubated for another 10 min at ambient temperature.

The fluorescence signal was measured using a FLx-800 microplate fluorescent reader

(Bio-tek Instruments, Inc.) at excitation wavelength of 360 nm and emission wavelength

of 460 nm.

Senescence Staining : HT-29 AP-1 cells were grown on cover slips and treated

with DMSO (control), individual dietary factors, or combinations of dietary factors. The

X-gal-based staining was performed using the Senescence Assay Kit (Sigma-Aldrich

Co.) according to the manufacturer’s instructions. Bluish-green stain was positive for

senescence-associated beta-galactosidase activity. The slides were fixed and images

were obtained using a Nikon Eclipse E600 microscope (Micron-Optics, Cedar Knolls,

NJ) equipped with DXM 1200 Nikon Digital Camera.

Statistical Analyses : Data are expressed as mean ± standard deviation, and

comparisons among treatment groups were made using one-way analysis of variance

(ANOVA) followed by a post hoc test for multiple comparisons – the Tukey's

Studentized Range Honestly Significant Difference (HSD) test. In all these multiple

comparisons, P < 0.05 was considered statistically significant. When only two groups of

treatment means were evaluated, we employed paired, two-tailed Student’s t-test

(P<0.01 was considered significant) or paired, one-tailed Student’s t-test (P<0.05 was

considered significant) as indicated in the text where applicable. Statistical analyses

100

were performed using SAS 9.1 software (SAS Institute Inc, NC) licensed to Rutgers

University.

4.4. Results

4.4.1. Transactivation of AP-1 luciferase reporter by combinations of SFN and

EGCG

As shown in Figure 4.1, treatment of HT-29 AP-1 cells for 24 hours with either SFN 25

μM, EGCG 20 μM or EGCG 100 μM individually resulted in about five-fold induction

of AP-1 luciferase activity as compared to control cells that were treated with DMSO.

Surprisingly, a low-dose combination of SFN 25 μM + EGCG 20 μM elicited a

dramatic induction of AP-1 luciferase activity (over 45-fold). In addition, a high-dose

combination of SFN 25 μM + EGCG 100 μM further potentiated the induction of AP-1

luciferase activity to about 175-fold. We also investigated the effects of pre-treatment

on the induction of AP-1 luciferase activity. In these experiments (data not shown), we

first pre-treated the HT-29 AP-1 cells for six hours with EGCG (20 μM or 100 μM),

then washed off the EGCG thrice with phosphate-buffered saline (PBS) and treated the

cells with SFN 25 μM for an additional 18 hours before assaying for luciferase activity.

Alternatively, we also pre-treated the cells with SFN 25 μM for 6 hours before washing

with PBS as above and treating with EGCG (20 μM or 100 μM) for an additional 18

hours. It was observed that there was no significant difference in induction of AP-1

luciferase activity in these pre-treatment experiments (data not shown) as compared to

when the two agents were co-treated as shown in Figure 4.1. This enabled us to rule out

any physicochemical interaction between the two agents in cell culture when co-treated

101

that may have otherwise produced any experimental artifacts in the luciferase assay.

Hence, since the effects of the combinations when co-treated were not physicochemical,

but potentially modulated at a mechanistic level, we continued all our experiments by

co-treating both agents together for 24 hours for ease of experimentation without

confounding variables.

4.4.2. SOD attenuates the synergism elicited by combinations of SFN and EGCG

Since EGCG is known to produce oxidative stress [14], we also investigated whether

the effects of the SFN and EGCG combinations on AP-1 luciferase induction were

mediated, in part, by the free radical scavenger SOD. Accordingly, we also co-treated

the combinations with 20U/ml of SOD before assaying for AP-1 luciferase activity.

Interestingly, the co-treatment with SOD significantly attenuated the induction observed

with the SFN + EGCG combinations in the HT-29 AP-1 cells. The relative luciferase

activity of the SFN 25 μM + EGCG 20 μM combination (over 45-fold) was attenuated

to about 2-fold in the presence of SOD; whereas the relative luciferase activity of the

SFN 25 μM + EGCG 100 μM combination (175-fold) was attenuated to about 15-fold

in the presence of SOD as shown in Figure 4.1 indicating that SOD signaling may play

a role in modulation of AP-1 luciferase activity by these chemopreventive

combinations.

102

4.4.3. Isobologram analyses and combination indices for the combinations of SFN

and EGCG

In order to confirm the synergistic interaction observed in the luciferase assays with the

combinations of SFN and EGCG, we performed isobologram analyses as reported by

Zhao et al [150]. Twenty-five combinations of SFN (2.5 μM, 5 μM, 7.5 μM, 10 μM,

12.5 μM) with EGCG (2.5 μM, 5 μM, 7.5 μM, 10 μM, 12.5 μM) were tested in addition

to the SFN 25 μM + EGCG 20 μM combination. Nine of these combinations that

showed same effect as individual agents in terms of five-fold induction of AP-1

luciferase activity were selected for isobologram analyses. Under the conditions of the

analyses, all the combinations tested showed synergistic interaction in the isobologram

analyses as shown in Figure 4.2. This confirmed the synergistic nature of the interaction

between the combinations and indicated that lower doses of SFN with EGCG would

also be able to elicit synergistic transactivation of the AP-1 luciferase reporter although

to a lesser degree. In addition, as reported by Zhao et al [150], we evaluated the

combination indices that were generated by these combinations in these analyses, and it

was observed that, in conformity with the general consensus, all the synergistic

combinations had a value of combination index <1 (ranging from 0.325 to 0.7, data not

shown) which further confirmed the synergistic interaction between the combinations of

SFN with EGCG.

4.4.4. Viability of the HT-29 AP-1 cells with the combinations of SFN and EGCG

In order to ascertain the effects of the combinations of SFN and EGCG on the cell

viability of the HT-29 AP-1 cells, we used the MTS assay with treatment durations of

103

24 hours and 48 hours. As shown in Figure 4.3, there was no significant change in cell

viability at 24 hours between the combination treatments and the individual agent

treatments relative to the control cells showing that the doses used were non-toxic to the

cells at 24 hours. At 48 hours, however, the viability of cells treated with combinations

comprising SFN 25 μM + EGCG 20 μM and SFN 25 μM + EGCG 100 μM decreased to

70% and 40% respectively relative to control cells indicating that the low-dose

combination of SFN 25 μM + EGCG 20 μM may be more appropriate to pursue in

longer duration in vitro studies or potential in vivo studies without seemingly toxic

effects a priori, and at the same time not compromising on the synergistic efficacy

elicited by the combination of these two chemopreventive agents.

4.4.5. Inhibition of EGCG-induced senescence by the combinations of SFN and

EGCG

Since EGCG is known to inhibit telomerase and induce senescence in leukemic cells

[151], we also investigated the effects of the combinations of SFN and EGCG on

senescence-associated beta-galactosidase activity by a standard staining procedure as

described in Materials and Methods. As shown in Figure 4.4, HT-29 AP-1 cells, when

cultured on cover slips and exposed to individual treatment of EGCG (20 μM or100

μM) for 24 hours, showed induction of senescence which was not observed in the case

of SFN 25 μM. Interestingly, on combining EGCG with SFN, the EGCG-associated

senescence was attenuated. Both combinations comprising SFN 25 μM + EGCG 20

μM, and, SFN 25 μM + EGCG 100 μM attenuated the cellular senescence induced by

EGCG suggesting that the synergistic effects of the combination on AP-1

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transactivation may also be potentially mediated in part via inhibition of cellular

senescence pathways.

4.4.6. Temporal gene expression profiles elicited by combinations of SFN and

EGCG and attenuation by SOD

We performed quantitative real-time PCR (qRT-PCR) experiments with primers (Table

4.1) for the luciferase gene to corroborate the synergism elicited with the combinations

of SFN and EGCG in the luciferase protein assay with mRNA levels in qRT-PCR. As

shown in Table 4.2, the temporal expression (at 6 hrs and 10 hrs) of luciferase gene in

qRT-PCR assays was significantly higher (P<0.01 at 6hrs by a two-tailed, paired

Student’s t-test ; and P<0.05 at 10hrs by a one-tailed, paired Student’s t-test) for the

combinations of SFN and EGCG as compared to individual dietary factor treatments in

consonance with our data in the luciferase protein assays. The treatment means for all

the treatment groups at a specific time point (6 hrs or 10 hrs) were significantly

different from each other ( P<0.05 by ANOVA and post hoc Tukey’s test for multiple

comparisons to detect significantly different means). In addition, co-treatment with

SOD attenuated the synergism elicited with the combinations of SFN and EGCG at both

6 hrs and 10 hrs as shown in Table 4.3 which also further validated our luciferase data.

We also determined by qRT-PCR the relative expression levels of transcripts of many

genes that were known to be either under the control of the AP-1 promoter or involved

in cell cycle regulation or cellular influx-efflux such as cyclin D1, cMyc, ATF-2, Elk-1,

SRF, CREB5, SLCO1B3, MRP1, MRP2 and MRP3 in the absence of SOD (Table 4.2)

and in the presence of SOD (Table 4.3).

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The low- and high-dose combinations of SFN and EGCG in this study elicited the

downregulation of positive cell cycle regulator cyclin D1 expression (Table 4.2) that

was further decreased by SOD (Table 4.3) as compared with individual dietary factors.

There was, however, no appreciable change in expression of cell proliferation-related

cMyc except for its downregulation in the high-dose combination in the presence of

SOD. In addition, transcription factors/coactivators that are known to be under the

control of the AP-1 promoter such as activating transcription factor (ATF-2), Ets-like

transcription factor (Elk-1), serum response factor (SRF) and cyclic AMP response

element binding protein 5 (CREB5) were also studied in the absence and presence of

SOD (Tables 4.2 and 4.3 respectively). Interestingly, the expression of ATF-2 with the

low-dose combination of SFN and EGCG was similar to that of the SFN-only treatment.

The activation of Elk-1 that was observed in our qRT-PCR studies (Table 4.2) was

similar for the combinations as well as individual agents which complemented our

results for phosphorylated ERK1/2 protein (Figure 4.5). Interestingly, the low-dose

combination of SFN and EGCG inhibited the transcriptional activation of SRF as

compared to individual dietary factors; this inhibition was reversed on co-treatment

with SOD at both time points. The combinations had no effect on transcriptional

expression of CREB5 as compared to individual agents. Since exogenous stress can

potentially stimulate the influx-efflux machinery of cells, we also investigated some key

transporter genes (Table 4.2). In this study, we observed the induction of the SLCO1B3

gene, which encodes for the organic anion transporter protein OATP1B3, by EGCG-

alone treatments which was reversed by treatment with the combinations of SFN and

EGCG (Table 4.2). Interestingly, the combinations of SFN and EGCG greatly induced

106

the expression of the efflux transporter MRP2 as shown in Table 4.2. In addition, the

expression of influx transporters MRP1 and MRP3 was lower for the combination-

treated cells as compared to the individual agent-treated cells.

4.4.7. Protein expression with the combinations of SFN and EGCG

We investigated the effects of the combinations on protein expression of major

mitogen-activated protein kinase (MAPK) pathway members including ERK, JNK and

p38 as well as the Akt pathway (Figure 4.5). Interestingly, although the expression of c-

jun N-terminal kinase (JNK) was greater for the combinations relative to the control, it

was not greater than the JNK expression of individual agents, suggesting that the

SFN+EGCG combination-mediated activation of AP-1 reporter may occur by cellular

mechanisms that are exclusive of JNK activation. Similarly, there was no significant

change in protein expression of extracellular signal regulated kinase (ERK), p38 or Akt

Ser and Akt Thr for the combination-treated cells as compared to the individual agent-

treated cells.

4.4.8. HDAC inhibitor Trichostatin A potentiates the synergism elicited by the low-

dose combination of SFN and EGCG

Since inhibition of histone deacetylase (HDAC) has been reported [141] as a novel

mechanism of chemoprotection by the isothiocyanate SFN, we investigated the effects

of HDAC inhibitor Trichostatin A (TSA) on the transactivation potential of the

combinations by pre-treatment of HT-29 AP-1 cells with 100ng/ml TSA for four hours

followed by treatment with dietary factors for 24 hours. Interestingly, as shown in

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Figure 4.6, pre-treatment with HDAC inhibitor TSA potentiated (about 20-fold) the

transactivation of the AP-1 luciferase reporter by SFN 25 μM alone, suggesting that

activation of AP-1 luciferase activity in this cell system may possibly relate to HDAC

inhibition. Similarly, a strong potentiation of AP-1 transactivation (about 88-fold) was

also observed with the low-dose combination of SFN 25 μM + EGCG 20 μM

suggesting that maximal transcriptional activation of the AP-1 reporter genes may

potentially be achieved by combining TSA with this synergistic combination. On the

other hand, transactivation by both EGCG 20 μM and EGCG 100 μM was not

potentiated by TSA inhibition. Interestingly, the high-dose combination of SFN 25 μM

+ EGCG 100 μM attenuated (about 93-fold) the synergism elicited with this

combination which may be attributed to toxicity caused by exposure to TSA in addition

to the high-dose combination.

4.4.9. Cytoplasmic and Nuclear HDAC Activity Assays for the combinations of

SFN and EGCG

HDAC activity assays for cytoplasmic and nuclear fractions of cells treated with dietary

factors were conducted as described under Materials and Methods at both 2 hours and

12 hours after treatment, as well as after first pre-treating the cells with 100ng/ml TSA

before treatment with dietary factors for 2 or 12 hours. Interestingly, there was no

significant change in HDAC activity of cytoplasmic or nuclear fractions of dietary

factor-treated cells relative to vehicle control (data not shown).

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4.4.10. Reactive oxygen species and SOD may modulate AP-1 transactivation by

the combinations of SFN and EGCG

Since EGCG is known to induce oxidative stress, we investigated the potential role of

reactive oxygen species (ROS) such as hydrogen peroxide (H2O2) along with the free

radical scavenger SOD in modulating the transcriptional events through the AP-1-

responsive reporter. Accordingly, we performed assays for H2O2 in cell-free media

using an assay kit as described in Materials and Methods. Consistent with the ability of

EGCG to induce oxidative stress, we quantified a production of 2 μM H2O2 at 15

minutes in HT-29 AP-1 cells treated with 20 μM EGCG alone. Presence of SFN in the

SFN 25 μM + EGCG 20 μM combination did not affect the amount of H2O2 produced

by EGCG. However, co-treatment with 20 U/ml of SOD decreased the amount of H2O2

detected at 15 minutes by half to 1 μM, which result was consistent in the case of both

EGCG 20 μM alone or SFN 25 μM + EGCG 20 μM as shown in Figure 4.7. The

amount of H2O2 detected decreased in a generally time-dependent manner thereafter for

most dietary factor treatments. In addition, about 4 μM H2O2 was produced at 15

minutes in HT-29 AP-1 cells treated with 100 μM EGCG alone or SFN 25 μM + EGCG

100 μM. However, co-treatment with 20 U/ml SOD was not able to significantly

scavenge the H2O2 produced at the high dose of EGCG or by the high-dose

combination of SFN 25 μM + EGCG 100 μM.

4.5. Discussion

Colorectal cancer has a natural history of transition from precursor to malignant lesion

that spans, on average, 15-20 years, providing a window of opportunity for effective

109

interventions and prevention [152]. Data accumulating in recent years have suggested

that aspirin, non-steroidal anti-inflammatory drugs, and selective cycloxygenase (COX-

2) inhibitors all have a potential to reduce both colorectal cancer and colorectal

adenomas [153], however, issues of safety and therapeutic indices have come up as

barriers to the use of some of these agents. Many dietary phytochemicals exhibit

beneficial effects to health including prevention of diseases such as cancer. Mammalian,

including human, cells respond to these dietary phytochemicals by “non-classical

receptor sensing” mechanism of electrophilic chemical-stress typified by “thiol

modulated” cellular signaling events primarily leading to gene expression of

pharmacologically beneficial effects, but sometimes unwanted cytotoxicity [149].

Indeed, with the ultimate goal of preventing cancer, science has advanced greatly in

better understanding cancer biology as also in identifying

chemotherapeutic/chemopreventive agents that would inhibit or delay the progression

of this disease. However, the need to maximally exploit the preventive or therapeutic

efficacy of agents without incurring toxicity to normal cells remains challenging. A

combinatorial approach to cancer therapy/prevention is being widely recognized as an

alternative strategy to potentially improve treatment success rates. Recently [154], a

Phase I Trial of sorafenib in combination with IFN α-2a was conducted in patients with

unresectable and/or metastatic renal cell carcinoma or malignant melanoma. Besides,

combination therapy of an orthotopic renal cell carcinoma model using intratumoral

vector-mediated costimulation and systemic interleukin-2 was recently reported [155].

Interestingly, Adhami et al [156] recently reported combined inhibitory effects of green

tea polyphenols and selective COX-2 inhibitors on the growth of human prostate cancer

110

cells both in vitro and in vivo. Our laboratory has been studying two groups of dietary

phytochemical cancer chemopreventive compounds (isothiocyanates and polyphenols)

[1,2], which are effective in chemical-induced as well as genetically-induced animal

carcinogenesis models [3,157]. We decided to pursue the current study on colon cancer

prevention by examining the biologic modulation via AP-1 which is a group of dimeric

transcription factors composed of Jun, Fos, and ATF family proteins [158]. Notably, in

the present study, we investigated the combinations of two dietary factors –

isothiocyanate SFN and green tea polyphenol EGCG - and the role(s) mediated by

redox transcription factor AP-1 in modulating the anti-cancer potential of this putative

chemopreventive combination in stably transfected HT-29 AP-1 human colon

carcinoma cells.

We investigated the transactivation of the AP-1 luciferase reporter in our colon cancer

cell system by individual treatments with SFN and EGCG and with combinatorial

treatments of these two dietary factors (Fig. 1). Indeed, both low-dose and high-dose

combinations of SFN and EGCG synergistically induced transactivation of the AP-1

reporter as compared to individual dietary factors. This observation correlated with a

corresponding trend of luciferase gene induction in the quantitative real-time PCR

(qRT-PCR) assays (Table 4.2). We also confirmed the synergistic interaction in the

luciferase assays by testing various combinations of SFN and EGCG by isobologram

analyses and determining combination index values as reported by Zhao et al [150] as

shown in Figure 4.2. Studies in genetically modified mice and cells have highlighted a

crucial role for AP-1 in a variety of cellular events involved in normal development or

neoplastic transformation causing cancer [159]. Both gain- and loss-of-function studies

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have revealed specific roles for individual AP-1 components in cell proliferation,

differentiation, apoptosis, and other biological processes [158]. Recently, Maurer et al

[160] observed that in tumors with long necessary follow-up, such as colorectal cancer,

early-risk predictors would be needed, and provided first evidence for early prognostic

relevance of transcription factors including AP-1 differentially bound to the promoter

of the invasion-related gene u-PAR, and their molecular inducers, in colorectal cancer.

The synergistic transcriptional activation of the AP-1 reporter that we observe with the

combinations of SFN and EGCG in the present study may be seen in the light of the

above evidence that point to a singular role for AP-1 mediated transcriptional control of

potentially critical genes mediating cancer initiation and progression. This translates

into potentially greater efficacy, of the combination of SFN and EGCG in

chemoprevention of cancer. Interestingly, co-treatment with free radical scavenger SOD

attenuated the synergism elicited by the combinations. This observation also

corroborated with a corresponding attenuation of luciferase gene transcript in the qRT-

PCR assays (Table 4.3). Indeed, assays for H2O2 in cell-free media (Figure 4.7) revealed

that SOD co-treatment decreased the amount of H2O2 noted with the low-dose

combination of SFN and EGCG by half. Taken together, these above observations point

to a potential role for SOD signaling in modulating the pharmacologic activity of the

combination of SFN and EGCG. Additional studies are necessary to better understand

and delineate the specific pathway cross-talk with AP-1 signaling.

The downregulation of cyclin D1 by the SFN and EGCG combinations may be related

to the intrinsic ability of SFN to induce G1 cell cycle arrest in HT-29 cells as we have

reported earlier [84]. ATF-2 can form a heterodimer with c-jun and controls the

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induction of c-jun in an AP-1 independent manner, however, both ATF-2 and c-jun can

be activated by c-Jun N-terminal kinases (JNK) [161]. The absence of major ATF-2

activation with the combinations in our study may thus also relate to the absence of

JNK activation that we observed with the combinations as compared to individual

dietary factors (Figure 4.5). Biochemical studies have indicated that Elk-1 is a good

substrate for ERKI/ERK2 in vitro, and that the kinetics of its modification correlated

well with MAPK activation in vivo [162]. Thus, the limited transcriptional (Table 4.2)

and translational (Figure 4.5) activation of Elk-1 and ERK1/2 respectively that we see

in this study may potentially be inter-related, although additional biochemical studies

will be necessary to substantiate this hypothesis. Because the influx-efflux machinery of

cells could be potentially turned on by exogenous stress, we also investigated some key

transporter genes (Table 4.2). Using Hagenbuch and Meier’s new nomenclature [163],

the gene encoding for the organic anion transporter protein OATP1B3 (old name

OATP8) is known as the SLCO1B3 (old nomenclature SLC21A8). OATP1B3 has been

shown to be expressed in various human cancer tissues as well as in different tumor cell

lines derived from gastric, colon, pancreas, gallbladder, lung and brain cancers [163].

The induction of SLCO1B3 that we observed with EGCG-alone treatments which was

reversed by treatment with the combinations of SFN and EGCG (Table 4.2) may be

relevant since the intracellular, pharmacologically active concentration of any drug is

the balance between uptake and neutralizing pathways, either by biotransformation or

extrusion from the targeted cells [164], although the pathobiological significance of

expression of this gene is not yet fully understood [163]. Interestingly, the combinations

of SFN and EGCG greatly induced the expression of the efflux transporter MRP2

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(Table 4.2) but downregulated the expression of influx transporters MRP1 and MRP3.

Since the combination treatment of SFN and EGCG would impose exogenous stress on

the cellular environment, the induction of MRP2 may be related to a cellular defense

response purported to increase the excretion/efflux of the xenobiotics or their

metabolites.

Cell senescence is broadly defined as the physiological program of terminal growth

arrest, which can be triggered by alterations of telomeres or by different forms of stress

[165]. Although senescent cells do not proliferate, they remain metabolically active and

produce secreted proteins with both tumor-suppressing and tumor-promoting activities

[165]. Besides apoptosis, cell proliferation could, thus, be limited by senescence [166].

In fact, it seems that activation of the senescence program and consequent permanent

growth arrest significantly contributes to the loss of the clonogenic capacity of tumor

cells and probably to tumor regression after anticancer therapy [166,167]. EGCG is

known to inhibit telomerase and induce senescence in leukemic cells [151] and we were

able to confirm this in HT-29 AP-1 cells as shown in Figure 4.4. Interestingly, the

combinations of SFN and EGCG inhibited the EGCG-induced senescence (Figure 4.4)

of HT-29 AP-1 cells. Recently [3], we demonstrated that ApcMin/+ mice fed with SFN-

supplemented diet developed significantly less and smaller polyps with higher apoptotic

and lower proliferative indices in their small intestine, in a SFN dose-dependent

manner. SFN also regulated different sets of genes involving apoptosis, cell

growth/maintenance and inflammation in the small intestinal polyps of ApcMin/+ mice

[168]. SFN also induced G(1) phase cell cycle arrest in HT-29 cells [84]. We have also

shown that EGCG treatment causes damage to mitochondria, and induces apoptotic cell

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death [14].Thus, the inhibition of cellular senescence we observed with the

combinations of SFN and EGCG may relate to the ability of SFN and EGCG to activate

apoptotic pathways that predominate over the senescence pathways induced by EGCG.

The viability (Figure 4.3) of the HT-29 AP-1 cells at 48 hours was about 70% and 40%

with the low- and high-dose combinations respectively since the low-dose combination

was not toxic to the cells as compared with the high-dose combination of SFN and

EGCG.

The effects of the combinations on protein expression (Figure 4.5) of major mitogen-

activated protein kinase (MAPK) pathway members including ERK, JNK and p38 as

well as the Akt pathway was not dramatic as compared to individual agents, suggesting

that the SFN+EGCG combination-mediated activation of AP-1 may occur by cellular

mechanisms that are exclusive of JNK activation. Since inhibition of histone

deacetylase (HDAC) has been reported [141] as a novel mechanism of chemoprotection

by the isothiocyanate SFN, we investigated the effects of HDAC inhibitor Trichostatin

A (TSA) on the transactivation potential of the combinations. Interestingly, TSA

potentiated (Figure 4.6) the synergism elicited by the low-dose combination of SFN and

EGCG leading us to speculate whether the strong AP-1 induction may relate to HDAC

inhibition. However, there was no significant change in HDAC activity of cytoplasmic

or nuclear fractions of dietary factor-treated cells (data not shown). Indeed, maximal

transcriptional activation of the AP-1 reporter genes may potentially be achieved by

combining TSA with the synergistic low-dose combination of SFN and EGCG. In

contrast, the synergism elicited with the high-dose combination was attenuated by TSA

(Figure 4.6) which may be attributed to toxicity caused by exposure to the high-dose

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combination together with TSA. Further empirical and heuristic studies are necessary to

elucidate the exact biochemical mechanisms and the nature of potential cross-talk

between AP-1 and HDAC from a physiological perspective.

We have previously reported [169] that the peak plasma concentration (Cmax)

achievable with SFN in rats was 20 μM after oral administration. In addition, we have

reported [22] that SFN 50 μM was toxic to HepG2 C8 cells, whereas SFN 25 μM was

suboptimal in its efficacy. Since a desirable objective of using combinatorial approaches

is to reduce the dose of the administered agents thereby reducing toxic side-effects, our

dose selection of 25 μM of SFN for the current study was guided by its proximity to the

observed Cmax and its suboptimal effectiveness in eliciting transcriptional effects as

compared to higher doses of SFN. Besides, in most studies, the concentrations needed to

observe the activities of EGCG typically range from 1 to 100 µM; these are, in reality,

concentrations that exceed those found in rodent and human plasma by 10- to 100-fold

[170,171]. However, the uptake of EGCG in HT-29 cells has also been shown to be

concentration-dependent in the range of 20-600 µM [170]. In addition, we have also

previously reported [14] that EGCG inhibited HT-29 cell growth with an IC50 of

approximately 100 µM. Accordingly, we elected to test two doses of EGCG (20 µM

and 100 µM) in the current study in combination with the 25 µM dose of SFN.

In summary, it is necessary to evolve and to justify alternative strategies to develop

agents that modulate multiple targets simultaneously with the aim of enhancing efficacy

or improving safety relative to agents that address only a single molecular target.

Combinatorial approaches to cancer chemoprevention lend themselves to the cause of

maximally exploiting the intrinsic ant-carcinogenic potential of known dietary factors

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with already proven beneficial effects individually. Taken together, the synergistic

activation of the AP-1 reporter that was potentiated by HDAC inhibitor TSA and

attenuated by free radical scavenger SOD point to a possible multifactorial control of

colon carcinoma that may involve a role for HDACs, inhibition of cellular senescence,

and SOD signaling. Future studies to delineate the complex regulation in biological

systems, as well as in vivo studies, would be useful in elucidating the effects of

combining dietary factors SFN and EGCG to better appreciate the pharmacological

benefits of this synergy in cancer prevention.


Sujit Nair is extremely grateful to Ms. Donna Wilson at the Keck Center for

Collaborative Neuroscience, Rutgers University, for exhaustive training and extensive

discussions that were very helpful in optimizing and validating the quantitative real-

time PCR assays. This work was supported in part by RO1-CA073674 and RO1-

CA092515 to Ah-Ng Tony Kong from the National Institutes of Health (NIH).

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Rel

ativ

e A

P-1

Luci

fera

se A

ctiv

ity

0

10

20

30

40

50

170

180

190

Ctrl S25

S25 +E20

S25 +E100

S25 +E20 +SOD

# *S25 +E100 +SOD

E20E100

#*

#

*

*#

Figure 4.1. Transactivation of AP-1 luciferase reporter by combinations of SFN

and EGCG, and attenuation by SOD

HT-29 AP-1 cells were seeded in six-well plates and treated with individual dietary

factors or with combinations of SFN and EGCG, as indicated, in the absence or

presence of 20U/ml SOD. The AP-1 luciferase activity was measured relative to vehicle

control (DMSO) after 24 hours of incubation and normalized against protein

concentration. Values represent mean ± standard deviation for three replicates, and are

representative of seven independent experiments. * P < 0.05, significantly different

from vehicle control (Ctrl) ; # P < 0.05, significantly different from each other.

118

SFN concentrations (uM)

0 5 10 15 20 25 30

EGC

G c

once

ntra

tions

(uM

)

0

5

10

15

20

25

Figure 4.2. Isobologram analyses of synergy between combinations of SFN and

EGCG.

Several combinations of individual dietary factors SFN and EGCG were analyzed for

synergy by the method of isobologram analysis as described elsewhere [150] and were

confirmed as synergistic. Data points are described by concentrations (in μM) of SFN

and EGCG reflected on x- and y-axes respectively, and are representative of three

independent experiments. The corresponding combination indices ranged from 0.325 to

0.7 (data not shown) which further confirmed the synergy between the combinations of

SFN and EGCG.

119

Control S25 E20 E100 S25+E20 S25+E100

%

Cel

l Via

bilit

y(R

elat

ive

to C

ontr

ol)

0

20

40

60

80

100

120

140

24hr 48hr

*

*

Figure 4.3. Viability of the HT-29 AP-1 cells with the combinations of SFN and

EGCG

HT-29 AP-1 cells were treated with individual dietary factors or with combinations of

SFN and EGCG for 24 hr or 48 hr as indicated and treated with MTS assay reagent to

ascertain cell viability. Values represent mean ± standard deviation for six replicates,

and are representative of three independent experiments. * P < 0.05, significantly

different from control.

120

Control E20 E100

S25 E20 + S25 E100 + S25

Figure 4.4. Inhibition of EGCG-induced senescence by the combinations of SFN

and EGCG

HT-29 AP-1 cells were cultured on cover slips and treated with individual dietary

factors or with combinations of SFN and EGCG for 24 hr. The cells were then fixed and

subjected to a histochemical stain for β-galactosidase activity following which they

were examined microscopically for senescence. Images are representative of three

independent experiments.

121

Figure 4.5. Protein expression with the combinations of SFN and EGCG


SFN and EGCG as indicated for 1 hr. Protein was harvested using a MAPK lysis buffer

for phosphorylated MAPK or with a whole cell lysis buffer for other proteins. The

proteins were immunoblotted using specific antibodies as indicated using actin as the

control. Blots are representative of three independent experiments.

122

Ctrl TSA S25 E20 E100 S25+E20,S25+E100

Rel

ativ

e A

P-1

Luci

fera

se A

ctiv

ity

0

20

40

60

80

100

120

140

160

180

200

Without TSAWith TSA

*

*

*

*

*

Figure 4.6. HDAC inhibitor Trichostatin A potentiates the synergism elicited by

the low-dose combination of SFN and EGCG

HT-29 AP-1 cells were seeded in six-well plates and pre-treated with 100ng/ml TSA for

4 hrs. Thereafter, they were additionally treated with individual dietary factors or with

combinations of SFN and EGCG as indicated for another 24 hrs. The AP-1 luciferase

activity was measured relative to vehicle control (DMSO) and normalized against

protein concentration. Values represent mean ± standard deviation for three replicates,


different from vehicle control (Ctrl).

123

Figure 4.7. Reactive oxygen species and SOD may modulate AP-1 transactivation

by the combinations of SFN and EGCG


SFN and EGCG as indicated both in the absence (7A) and presence (7B) of co-

treatment with 20U/ml SOD. Cell-free media were tested for H2O2 levels using only

H2O2 (10 μM) as a positive control in a fluorescence-based assay. For clarity of

presentation, only mean values of three replicates are shown that are representative of

three independent experiments.

Ctrl S25 E20 E100 E20+S25 E100+S25

H2O

2 (u

M)

0

1

2

3

4

5 15min 45min 90min 24hr

A

Ctrl S25 E20 E100 E20+S25 E100+S25

H2O

2 (u

M)

0

1

2

3

4

5

15min 45min 90min 24hr

B-with SOD

124

CHAPTER 5

Regulation of gene expression by a combination of dietary factors sulforaphane

and (-) epigallocatechin-3-gallate in PC-3 AP-1 human prostate adenocarcinoma

cells and Nrf2-deficient murine prostate13,14,15

5.1. Abstract

Although previous studies have addressed the role(s) of NRF2 or AP-1 in prostate

cancer, the potential for crosstalk between these two important transcription factors in

the etiopathogenesis of prostate cancer has not been explored thus far. We posited that

putative crosstalk may exist between NRF2 and AP-1 in prostate cancer on treatment

with dietary factors sulforaphane and (-) epigallocatechin-3-gallate in combination.

We performed in vitro studies in PC-3 AP-1 human prostate adenocarcinoma cells, in

vivo temporal (3 hr and 12 hr) microarray studies in the prostate of NRF2-deficient

mice, and in silico bioinformatic analyses to delineate conserved binding sites or

regulatory motifs in the promoter regions of NRF2 and AP-1, as well as coregulated

genes including ATF-2 and ELK-1. Downregulation (3 fold to around 35-fold) of key

genes identified as NRF2-dependent appeared to be the dominant response to oral

administration of the SFN+EGCG combination at both 3 hr and 12 hr, which was in

13Work described in this chapter is under consideration for publication as Nair, S., Cai, L., Kong AN. 14Keywords: sulforaphane, EGCG, Nrf2, AP-1, microarray, prostate cancer 15Abbreviations : NRF2, Nuclear Factor-E2-related factor 2; AP-1, activator protein-1; ATF-2, activating transcription factor 2; SFN, sulforaphane; EGCG, (-) epigallocatechin-3-gallate; MAPK, mitogen-activated protein kinase; qRT-PCR, quantitative real-time PCR.

125

consonance with the diminished induction of the AP-1 luciferase reporter by the

SFN+EGCG combination seen in our in vitro studies. Our current transcriptional

regulation study identifying NRF2- and AP-1-coregulated genes provides a discursive

framework for appreciating putative crosstalk between NRF2 and AP-1 on treatment

with the SFN+EGCG combination in prostate cancer and may help to identify new

markers for screening or targets for therapy. Taken together, it appears a priori that the

combination of SFN+EGCG may hold promise for patients in the management of

prostate cancer via concerted modulation of NRF2 and AP-1 pathways.

5.2. Introduction

Prostate cancer, according to the Centers for Disease Control and Prevention (CDC)

[130], is the second leading cause of cancer deaths among men in the United States, and

the seventh leading cause of deaths overall for men. The incidence of prostate cancer in

the United States has increased by 1.1% per year from 1995–2003 [130,172]. In

addition, the National Cancer Institute (NCI)’s Surveillance Epidemiology and End

Results (SEER) Statistics Fact Sheets [173] show that, based on rates from 2002–2004,

16.72% of men born today will be diagnosed with cancer of the prostate at some time

during their lifetime, i.e., 1 in 6 men in the United States are at a lifetime risk of

developing prostate cancer. In a recent study [174], the prostate-specific antigen (PSA)

nadir while intermittently taking a testosterone-inactivating pharmaceutical agent was

determined to be the best predictor of prostate cancer-specific mortality. Nevertheless,

despite the considerable attention given to PSA as a screening test for prostate cancer, it

126

is needle biopsy, and not the PSA test result, that actually establishes the diagnosis of

prostate cancer [175].



factor 2 that has been extensively studied by many investigators including us as noted

elsewhere [149,176]. Nrf2 is indispensable to cellular defense against many chemical

insults of endogenous and exogenous origin, which play major roles in the

etiopathogenesis of many cancers as well as inflammatory bowel disease [177] and

Parkinson’s disease [44]. Rushmore et al [178] were the first to identify a core

antioxidant response element (core ARE or cARE) sequence 5′–RGTGACNNNGC–3′

responsible for transcriptional activation by xenobiotics, that was later expanded by

Wasserman and Fahl [179] giving rise to an expanded ARE (eARE) sequence described

by 5’-TMAnnRTGAYnnnGCRwwww-3’. Recently, Nerland [180] noted that

nucleotides previously considered to be degenerate also contribute to the binding of the

Nrf2 heterodimer to the response element. We observed [18] that the induction of

antioxidant response element (ARE)-regulated genes in vitro in human prostate cancer

PC-3 cells upon treatment with phenethyl isothiocyanate (PEITC) is associated with the

activation of extracellular signal-regulated kinase (ERK) and c-jun N-terminal kinase

(JNK) resulting in the phosphorylation and nuclear translocation of Nrf2. More

recently, Watai et al [181] showed that endogenous Keap1, which acts as a regulator of

Nrf2 activity through an interaction with the Nrf2 Neh2 domain, remains mostly in the

cytoplasm, and electrophiles promote nuclear accumulation of Nrf2 without altering the

127

subcellular localization of Keap1. Thus, the Keap1-Nrf2-ARE axis potentially has an

important role to play in various forms of cancer including that of the prostate.

Transcription factor activator protein-1 (AP-1) regulates a wide range of cellular

processes, including cell proliferation, death, survival and differentiation [182]. AP-1 is

a redox-sensitive transcription factor that senses and transduces changes in cellular

redox status and modulates gene expression responses to oxidative and electrophilic

stresses presumably via sulfhydryl modification of critical cysteine residues found on

this protein and/or other upstream redox-sensitive molecular targets [149]. AP-1 is

composed of heterodimeric protein complexes of members of the basic leucine zipper

(bZIP) protein families, including the Jun (c-Jun, JunB and JunD) and Fos (c-Fos, FosB,

Fra-1 and Fra-2) families, Maf (c-Maf, MafB, MafA, Maf G/F/K and Nrl), Jun

dimerization partners (JDP1 and JDP2) and the closely related activation transcription

factor (ATF; ATF2, LRF1/ATF3 and B-ATF) subfamilies [182-184] which recognize

either 12-O-tetradecanoylphorbol-13-acetate (TPA) response elements (TRE, 5'-

TGAG/CTCA-3') or cAMP response elements (CRE, 5'-TGACGTCA-3') [185].

Recently, we showed that ERK and JNK signaling pathways are involved in the

regulation of AP-1 and cell death elicited by three isothiocyanates (SFN; PEITC; and

allyl isothiocyanate, AITC) in human prostate cancer PC-3 cells [184].

Evidence gleaned from epidemiological studies has revealed an inverse correlation

between the intake of cruciferous vegetables and the risk of certain types of cancer

[136]. Isothiocyanates are a chemical class of compounds that are not naturally present

in cruciferous vegetables, such as broccoli and cauliflower, but are nevertheless

generated from hydrolysis of secondary metabolites known as glucosinolates by the

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enzyme myrosinase during the process of vegetable crushing or mastication [139]. They

may also be produced in the intestines where resident microflora can promote the

hydrolysis of glucosinolates to isothiocyanates [140]. Sulforaphane (SFN), a dietary

phytochemical obtained from broccoli, has been implicated in several physiological

processes consistent with anticarcinogenic activity, including enhanced xenobiotic

metabolism, cell cycle arrest, and apoptosis [186]. Epigenetic changes associated with

inhibition of histone deacetylase (HDAC) activity [187] constitute one mechanism of

cancer chemoprevention by SFN. Indeed, SFN has been shown [188] to retard the

growth of human PC-3 xenografts and inhibit HDAC activity in human subjects.

Recently, we reported [17] that SFN induces hemoxygenase-1 (HO-1) by activating the

antioxidant response element (ARE) through the induction of Nrf2 protein in HepG2

cells, and that overexpression of all four p38 mitogen-activated protein kinase (MAPK)

isoforms negatively regulated the constitutive and inducible ARE-dependent gene

expression. It has also been reported [189] that SFN-induced cell death in PC-3 and

DU145 human prostate cancer cells is initiated by reactive oxygen species and that

induction of autophagy [190] represents a defense mechanism against SFN-induced

apoptosis in PC-3 and LNCaP human prostate cancer cells. In addition, we have

observed [80] that SFN suppresses the transcriptional activation of NFkappaB as well as

NFkappaB-regulated gene expression in PC-3 cells via inhibition of IKKbeta

phosphorylation as well as IkappaBalpha phosphorylation and degradation.

The water-extractable fraction of green tea contains abundant polyphenolic compounds,

in which (-) epigallocatechin-3-gallate (EGCG) is the major constituent (>50% of

polyphenolic fraction) [143]. We have observed [14] that EGCG treatment causes

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damage to mitochondria, and that JNK mediates EGCG-induced apoptotic cell death in

HT-29 human colon cancer cells. EGCG is also reported [147,148] to inhibit DNA

methyltransferase with demethylation of the CpG islands in the promoters, and to

reactivate methylation-silenced genes such as p16INK4a, retinoic acid receptor beta,

O6-methylguanine methyltransferase, human mutL homolog 1, and glutathione S-

transferase-pi in human colon cancer HT-29 cells, esophageal cancer KYSE 150 cells,

and prostate cancer PC-3 cells. Recently, it was noted [191] that EGCG suppresses

early stage, but not late stage, prostate cancer in TRAMP (Transgenic Adenocarcinoma

Mouse Prostate) animals without incurring undue toxicity. EGCG has also been shown

to induce growth arrest and apoptosis in NRP-152 and NRP-154 rat prostate epithelial

cells [192]. Besides, EGCG has been reported [193] to modulate the

phosphatidylinositol-3-kinase/protein kinase B- and MAPK-pathways in DU145 and

LNCaP human prostate cancer cells, and to have combined inhibitory effects with

selective cyclooxygenase-2 inhibitors [156] on the growth of human prostate cancer

cells both in vitro and in vivo. We have also shown [55] that a greater number of Nrf2-

regulated genes are modulated in murine liver on oral administration of EGCG than in

small intestine.



carcinogenesis [43]. In the present study, we investigated via transcriptome profiling the

gene expression changes induced by a combination of dietary factors SFN and EGCG in

Nrf2-deficient mice, the in vitro effects of this combination in PC-3 AP-1 cells, and in

silico bioinformatic analyses to delineate conserved binding sites or regulatory motifs in

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the promoter regions of NRF2 and AP-1, as well as coregulated genes including ATF-2

and ELK-1. We demonstrate that the effects of the combination of SFN+EGCG in

prostate cancer may be mediated via concerted modulation of NRF2 and AP-1

pathways.


Cell culture and Reagents : Human prostate cancer PC-3 cells were stably

transfected with an Activator Protein (AP-1) luciferase reporter construct, and are

referred to as PC-3 AP-1 cells or PC-3 C9 cells. The cells were cultured in Minimum

Essential Medium (MEM) containing 10% Fetal Bovine Serum (FBS) and 1%

Penicillin-Streptomycin. Twelve hours prior to experimental treatments, the cells were

exposed to MEM containing 0.5% FBS. Sulforaphane (SFN) was obtained from LKT

Labs (St. Paul, MN) ; whereas (-) epigallocatechin-3-gallate (EGCG) and superoxide

dismutase (SOD) were obtained from Sigma-Aldrich (St.Louis, MO). Both SFN and

EGCG were dissolved in dimethylsulfoxide (DMSO, Sigma), whereas SOD was

dissolved in 1X phosphate-buffered saline (PBS).

Reporter gene assays : PC-3 AP-1 cells were seeded in six-well culture plates

and treated in duplicate with dimethylsulfoxide (control), 20 μM EGCG, 100 μM

EGCG, 25 μM SFN, 20 μM EGCG + 25 μM SFN, or 100 μM EGCG + 25 μM SFN for

24 hours. Thereafter, the supernatant medium was aspirated on ice, cells were washed

thrice with ice-cold 1X PBS, treated with 1X Luciferase Reporter Lysis Buffer

(Promega) and subjected to one cycle of snap freeze-thaw at -80 °C. Cell lysates were

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harvested with sterile RNAse-free and DNAse-free cell scrapers into microcentrifuge

tubes that were immediately placed on ice. They were then centrifuged at 4 °C for ten

minutes at 13000 x g and returned to ice. Twenty microliters of supernatant solution

was analyzed for relative luciferase activity using a Sirius Luminometer (Berthold

Detection Systems). The relative luciferase activities were normalized by protein

concentrations of individual samples as described below.

Protein Assays : Protein concentrations of samples were determined by the

bicinchonic acid-based BCA Protein Assay Kit (Pierce) according to the manufacturer’s

instructions using a 96-well plate. Standard curves were constructed using bovine serum

albumin (BSA) as a standard. The sample readings were obtained on a μQuant

microplate reader (Bio-tek Instruments, Inc.) at 560nm.

Cell Viability Assays : The cell viability assays were performed in 24-well cell

culture plates using MTS Assay Kit (Promega) according to the manufacturer’s

instructions. Cell viability was determined at both 24 and 48 hours after treatment with

dietary factors. The absorbance readings were obtained on an μQuant microplate reader

(Bio-tek Instruments, Inc.) at recommended wavelength of 490nm.

RNA Extraction and Assessment of RNA Integrity : PC-3 AP-1 cells were

subjected to treatment with different dietary factors in triplicate for 6hours or 10 hours.

RNA was harvested using the RNeasy Mini Kit (Qiagen) according to the

manufacturer’s instructions. RNA integrity was assessed using formaldehyde gels in 1X

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MOPS buffer and RNA concentration was determined by the 260/280 ratio on a DU

530 UV/Visible spectrophotometer (Beckman).

Quantitative Real-time PCR Assays : Several genes of interest including

luciferase gene as well as genes known to be either under the control of the AP-1

promoter or involved in cell cycle regulation or cellular influx-efflux such as cyclin D1,

cMyc, ATF-2, Elk-1, SRF, CREB5, MDR1, SLCO1B3, MRP1, MRP2 and MRP3 were

selected for quantitative real-time PCR analyses. Beta-actin served as the

“housekeeping” gene. The specific primers for these genes as we have reported

previously [194] were designed using Primer Express 2.0 software (Applied



Biotechnology Information Blast search of the human genome. For the real-time PCR

assays, briefly, after the RNA extraction and assessment of RNA integrity, first-strand










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The gene expression was determined by normalization with control gene beta-actin.

Promoter Analyses for Transcription Factor Binding Sites (TFBS) : The

promoter analyses were performed in the laboratory of Dr. Li Cai (Department of

Biomedical Engineering, Rutgers University) using Genomatix MatInspector [195,196].

Briefly, human promoter sequences of NRF2, AP-1, ATF-2 and ELK-1, or

corresponding murine promoter sequences, were retrieved from Gene2Promoter

(Genomatix). Comparative promoter analyses were then performed by input of these

sequences in FASTA format into MatInspector using optimized default matrix

similarity thresholds. The similar and/or functionally related TFBS were grouped into

‘matrix families’ and graphical representations of common TFBS were generated. The

‘V$’ prefixes to the individual matrices are representative of the Vertebrate

MatInspector matrix library. We also elucidated common regulatory sequences in

promoter regions of human, or murine, NRF2 and AP-1. The ‘core sequence’ of a

matrix is defined as the (usually four) highest conserved positions of the matrix that is

provided in upper case letters in our Tables. The maximum core similarity of 1.0 is only

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reached when the highest conserved bases of a matrix match exactly in the sequence.

Only matches that contain the "core sequence" of the matrix with a score higher than the

core similarity are listed in the output.



knockout mice Nrf2 (-/-) (C57BL/SV129) have been described previously [100]. Nrf2 (-








products. Male C57BL/6J/Nrf2(-/-) mice from third generation of backcross were used

in this study. Age-matched male C57BL/6J mice were purchased from The Jackson

Laboratory (Bar Harbor, ME). Mice in the age-group of 9-12 weeks were housed at

Rutgers Animal Facility with free access to water and food under 12 h light/dark cycles.

After one week of acclimatization, the mice were put on AIN-76A diet (Research Diets

Inc. NJ USA) for another week. The mice were then administered both SFN (LKT

Labs, St. Paul, MN) and EGCG (Sigma-Aldrich, St.Louis, MO) at doses of 45 mg/kg

and 100mg/kg respectively (dissolved in 50% PEG 400 aqueous solution) by oral

gavage. The control group animals were administered only vehicle (50% PEG 400

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aqueous solution). Each treatment was administered to a group of four animals for both

C57BL/6J and C57BL/6J/Nrf2(-/-) mice. Mice were sacrificed at either 3h or 12hr after

dietary factor treatment or vehicle administration (control group). The prostates of the

animals were retrieved and stored in RNA Later (Ambion, Austin,TX) solution.

Microarray Sample Preparation and Hybridization : Total RNA from

prostate tissues was isolated by using TRIzol (Invitrogen, Carlsbad, CA) extraction

coupled with the RNeasy kit from Qiagen (Valencia, CA). Briefly, tissues were

homogenized in trizol and then extracted with chloroform by vortexing. A small volume

(1.2 ml) of aqueous phase after chloroform extraction and centrifugation was adjusted

to 35% ethanol and loaded onto an RNeasy column. The column was washed, and RNA

was eluted following the manufacturer’s recommendations. RNA integrity was

examined by electrophoresis, and concentrations were determined by UV

spectrophotometry. Affymetrix (Affymetrix, Santa Clara, CA) mouse genome 430 2.0

array was used to probe the global gene expression profiles in mice following

SFN+EGCG treatment. The mouse genome 430 2.0 Array is a high-density

oligonucleotide array comprised of over 45,101 probe sets representing over 34,000

well-substantiated mouse genes. The library file for the above-mentioned

oligonucleotide array is readily available athttp://www.affymetrix.com/support/technical/

libraryfiles.main.affx. After RNA isolation, the subsequent technical procedures

including quality control and estimation of RNA concentration, cDNA synthesis and

biotin-labeling of cRNA, hybridization and scanning of the arrays, were performed at

CINJ Core Expression Array Facility of Robert Wood Johnson Medical School (New

http://www.affymetrix.com/support/technical/libraryfiles.main.affx

http://www.affymetrix.com/support/technical/libraryfiles.main.affx

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Brunswick, NJ). Each chip was hybridized with cRNA derived from a pooled total

RNA sample from four mice per treatment group, per time-point, and per genotype (a

total of eight chips were used in this study). Briefly, double-stranded cDNA was

synthesized from 5 µg of total RNA and labeled using the ENZO BioArray RNA

transcript labeling kit (Enzo Life Sciences, Inc.,Farmingdale, NY, USA) to generate

biotinylated cRNA. Biotin-labeled cRNA was purified and fragmented randomly

according to Affymetrix’s protocol. Two hundred microliters of sample cocktail

containing 15 μg of fragmented and biotin-labeled cRNA was loaded onto each chip.

Chips were hybridized at 45°C for 16 h and washed with fluidics protocol EukGE-

WS2v5 according to Affymetrix’s recommendation. At the completion of the fluidics

protocol, the chips were placed into the Affymetrix GeneChip Scanner where the

intensity of the fluorescence for each feature was measured.

Microarray Data Analyses : The microarray data analyses were performed in

the laboratory of Dr. Li Cai (Department of Biomedical Engineering, Rutgers

University). The CEL file created from each sample (chip) was first imported into

dChip software [197,198] for further data characterization. Briefly, a gene information

file with current annotations and functional gene ontology was generated and the

Affymetrix Chip Description File (CDF) was specified. The data were then normalized

in dChip and the expression value for each gene was determined by calculating the

average of differences in intensity (perfect match intensity minus mismatch intensity)

between its probe pairs. The expression values were imported into GeneSpring 7.2

(Agilent Technologies, Inc., Palo Alto, CA) followed by data filtration based on flags

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present in at least one of the samples, and a corresponding gene list based on those flags

was generated. Lists of genes that were either induced or suppressed more than three

fold between treated versus vehicle group of same genotype were created by filtration-

on-fold function within the presented flag list. By use of Venn Diagram function, lists

of genes that were regulated more than three fold only in prostate of C57BL/6J mice but

not in prostate of C57BL/6J/Nrf2(-/-) mice at both 3h and 12h were generated, and were

designated as Nrf2-dependent genes. Using a Unix-based program at Dr. Li Cai’s

laboratory, the Affymetrix Probe Set IDs for the Nrf2-dependent genes thus identified

were matched against the "all genes" expression values list for these set of samples, and

expression values for these Affymetrix Probe Set IDs were retrieved. This ‘external

data’ was then imported into dChip whereupon Clustering and Enrichment Analysis

was performed to obtain hierarchical tree clustering diagrams for the Nrf2-dependent

genes. This clustering provided functional classification of Affymetrix Probe Set IDs

and gene descriptions that were then matched with the GeneSpring-generated Nrf2-

dependent Affymetrix Probe Set IDs with fold-change values. Quantitative real-time

PCR assays as described earlier were performed on several genes to validate the

microarray results.

Statistical Analyses : Data are expressed as mean ± standard deviation, and

comparisons among treatment groups were made using one-way analysis of variance

(ANOVA) followed by a post hoc test for multiple comparisons – the Tukey's

Studentized Range Honestly Significant Difference (HSD) test. In all these multiple

comparisons, P < 0.05 was considered statistically significant. In order to validate the

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microarray results, the correlation between corresponding microarray data and real-time

PCR data was evaluated by the statistical ‘coefficient of determination’, r2 = 0.96.

Statistical analyses were performed using SAS 9.1 software (SAS Institute Inc, NC)

licensed to Rutgers University.

5.4. Results

5.4.1. Diminished transactivation of AP-1 luciferase reporter by combinations of

SFN and EGCG

As shown in Figure 4.1A, treatment of PC-3 AP-1 cells for 24 hours with either EGCG

20 μM, EGCG 100 μM or SFN 25 μM individually, resulted in variable induction of

AP-1 luciferase activity as compared to control cells that were treated with DMSO.

Surprisingly, a low-dose combination of SFN 25 μM + EGCG 20 μM elicited a

diminished induction of AP-1 luciferase activity (less than 5-fold). In addition, a high-

dose combination of SFN 25 μM + EGCG 100 μM further diminished the induction of

the AP-1 luciferase reporter. We also investigated the effects of pre-treatment on the

induction of AP-1 luciferase activity. In these experiments (data not shown), we first

pre-treated the PC-3 AP-1 cells for six hours with EGCG (20 μM or 100 μM), then

washed off the EGCG thrice with phosphate-buffered saline (PBS) and treated the cells

with SFN 25 μM for an additional 18 hours before assaying for luciferase activity.

Alternatively, we also pre-treated the cells with SFN 25 μM for 6 hours before washing

with PBS as above and treating with EGCG (20 μM or 100 μM) for an additional 18

hours. It was observed that there was no significant difference in induction of AP-1

luciferase activity in these pre-treatment experiments (data not shown) as compared to

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when the two agents were co-treated as shown in Figure 4.1A. This enabled us to rule

out any physicochemical interaction between the two agents in cell culture, when co-

treated, that may have otherwise produced any experimental artifacts in the luciferase

assay. Hence, since the effects of the combinations when co-treated were not

physicochemical, but potentially modulated at a mechanistic level, we continued co-

treating both agents together for a duration of 24 hours for ease of experimentation

without confounding variables.

5.4.2. Viability of the PC-3 AP-1 cells with the combinations of SFN and EGCG

In order to ascertain the effects of the combinations of SFN and EGCG on the cell

viability of the PC-3 AP-1 cells, we used the MTS assay with treatment durations of 24

hours and 48 hours. As shown in Figure 4.1B, the cell viability at 24 hours for the low-

dose combination treatment of SFN 25 μM + EGCG 20 μM was about 75 to 80 %,

whereas it was about 60% for the high-dose combination of SFN 25 μM + EGCG 100

μM. The low-dose combination of SFN 25 μM + EGCG 20 μM may be more

appropriate to pursue in longer duration in vitro studies or potential in vivo studies

without seemingly toxic effects a priori, and at the same time not compromising on the

efficacy elicited by the combination of these two chemopreventive agents.

5.4.3. Temporal gene expression profiles elicited by combinations of SFN and

EGCG

We performed quantitative real-time PCR (qRT-PCR) experiments with primers for the

luciferase gene to corroborate the synergism elicited with the combinations of SFN and

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EGCG in the luciferase protein assay with mRNA levels in qRT-PCR. As shown in

Figure 5.2, the temporal expression (at 6 hrs and 10 hrs) of luciferase gene in qRT-PCR

assays was lower for the combinations of SFN and EGCG as compared to individual

dietary factor treatments in consonance with our data in the luciferase protein assays.

The treatment means for all the treatment groups at a specific time point (6 hrs or 10

hrs) were significantly different from each other (P<0.05 by ANOVA and post hoc

Tukey’s test for multiple comparisons to detect significantly different means). We also

determined by qRT-PCR the relative expression levels of transcripts of many genes that

were known to be either under the control of the AP-1 promoter or involved in cell

cycle regulation or cellular influx-efflux such as cyclin D1, cMyc, ATF-2, ELK-1, SRF,

CREB5, SLCO1B3, MRP1, MRP2 and MRP3 (Figure 5.2).

The low- and high-dose combinations of SFN and EGCG in this study elicited the

downregulation of positive cell cycle regulator cyclin D1 expression as compared with

individual dietary factors especially at 10 hr. There was, however, no appreciable

change in expression of cell proliferation-related cMyc Binding Protein. In addition,

transcription factors/coactivators that are known to be under the control of the AP-1

promoter such as activating transcription factor (ATF-2), Ets-like transcription factor

(ELK-1), serum response factor (SRF) and cyclic AMP response element binding

protein 5 (CREB5) were also studied. Interestingly, both ATF-2 and ELK-1 were

significantly downregulated by the combinations of SFN and EGCG as compared to

individual dietary factors. Besides, the low-dose combination of SFN and EGCG at 6hr

(and the high-dose combination at 10hr) inhibited the expression of SRF as compared to

individual dietary factors. Similarly, the combinations inhibited the expression of

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CREB5 as compared to individual agents. Since exogenous stress can potentially

stimulate the influx-efflux machinery of cells, we also investigated some key transporter

genes (Figure 5.2). In this study, we observed that the combinations of SFN and EGCG

inhibited the expression of the SLCO1B3 gene, which encodes for the organic anion

transporter protein OATP1B3, whereas the combinations did not have any significant

effect on the expression of MDR1 gene. Interestingly, the combinations of SFN and

EGCG greatly induced the expression of the efflux transporter MRP2 as compared to

individual agents as shown in Figure 5.2. In addition, the expression of influx

transporters MRP1 and MRP3 was not significantly different for the combination-

treated cells as compared to the individual agent-treated cells.

5.4.4. Comparative promoter analyses of NRF2 and AP-1, as well as ATF-2 and

ELK-1, for conserved Transcription Factor Binding Sites (TFBS)

We performed comparative analyses of NRF2 and AP-1 human promoter sequences as

described in Materials and Methods. We also studied Nrf2 and AP-1 murine promoter

sequences similarly. Table 5.1A is an alphabetical listing of the conserved vertebrate

(V$) matrix families between these two transcription factors. The major human families

included Activator protein 4 and related proteins, cell cycle regulators, E-box binding

factors, human and murine ETS1 factors, fork head domain factors, hypoxia inducible

factor, myc-associated zinc fingers, nuclear respiratory factor 1, serum response

element binding factor, and signal transducer and activator of transcription amongst

others. Interestingly, nuclear factor kappa B (NFkB) was conserved in human sequences

of Nrf2 and AP-1. We also performed comparative promoter analyses on ATF-2 and

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ELK-1, which we had previously identified (Figure 5.2) as AP-1-regulated genes. Table

5.1B alphabetically lists the conserved vertebrate (V$) matrix families between ATF-2

and ELK-1 that have been pictorially represented in Figure 5.3. Some key conserved

matrix families included AP-1, cyclic AMP-responsive element binding proteins,

estrogen response elements, human and murine ETS1 factors, fork head domain factors,

farnesoid-X-activated receptor response elements, human acute myelogenous leukemia

factors, Ikaros zinc finger family, myc-associated zinc fingers, nuclear factor of

activated T-cells, nuclear factor kappa B (NFkB), peroxisome proliferators-activated

receptor, ras-responsive element binding protein, serum response element binding

factor, signal transducer and activator of transcription, and X-box binding factors

amongst others. Interestingly, as is evident from Tables 5.1A and 5.1B, several key

matrix families were conserved not just between NRF2 and AP-1, or between the AP-1-

regulated genes ATF-2 and ELK-1, in either human or murine species, but there was

also some degree of overlap between TFBS identified in Tables 5.1A and 5.1B.

Furthermore, as shown in Table 5.2, we identified matrices (individual matrix family

members) with conserved regulatory sequences in promoter regions of NRF2 and AP-1

in either human or murine species. Multiple matches that were elicited with the same

core sequence have also been grouped together and listed in Table 5.2.

5.4.5. Temporal microarray analyses of genes modulated by SFN+EGCG

combination in the prostate of NRF2-deficient mice

Table 5.3 lists the genes that were downregulated at both 3hr and 12 hr by the

SFN+EGCG combination in the prostate of NRF2-deficient mice according to their

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biological functions. Interestingly, downregulation of genes appeared more important

in the prostate of these mice, since the upregulation of genes was negligible (data not

shown). Indeed, a strong degree of downregulation ranging from 3 to around 35 fold

was observed in vivo. This was also in consonance with our in vitro results in Figure 5.1

where the combination of SFN+EGCG elicited diminished activation of the luciferase

reporter. Furthermore, several genes that were downregulated in our in vivo study were

also common to our regulatory comparative promoter analyses between NRF2 and AP-

1, and ATF-2 and ELK-1, as described earlier. These included Ikaros family zinc

fingers, forkhead box members, and ATF-2 amongst others. Interestingly, several

coactivators and corepressors of NRF2, as well as NRF3, and the adenomatosis

polyposis coli (Apc) gene, were also shown to be modulated via NRF2 in response to

the combination of SFN+EGCG.

5.5. Discussion

Expression profiling and proteomics have the potential to transform the management of

prostate cancer, identifying new markers for screening, diagnosis, prognosis,

monitoring and targets for therapy [199]. Although several studies have addressed the

putative role(s) of either NRF2 or AP-1 per se in prostate cancer, the potential for

putative crosstalk between these two important transcription factors in the

etiopathogenesis of prostate cancer has not been explored thus far. We have used a

multi-pronged approach consisting of in vitro studies in prostate cancer PC-3 AP-1

cells, in vivo studies in the prostate of NRF2-deficient mice, and bioinformatic tools to

elucidate conserved motifs in the promoter regions of these transcription factors, as well

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as genes coregulated by them. We, thus, demonstrate the potential for putative crosstalk

between NRF2 and AP-1 in prostate cancer on treatment with dietary factors SFN and

EGCG in combination.

Interestingly, from our in vitro data in PC-3 AP-1 cells (Figure 5.1A), we observed a

diminished induction of the luciferase reporter on treatment with a combination of

SFN+EGCG that was dose-dependent. We observed similar trends in our in vivo

microarray data in NRF2-deficient mice (Table 5.3), where downregulation (3 fold to

around 35-fold) of key genes identified as NRF2-dependent appeared to be the

dominant response to oral administration of the SFN+EGCG combination at both 3 hr

and 12 hr. Quantitative real-time PCR analyses in our in vitro system (Figure 5.2)

confirmed that several genes including ATF-2 and ELK-1 were regulated by AP-1.

Bioinformatic analyses of the promoter regions of NRF2 and AP-1, as well as ATF-2

and ELK-1, revealed an interesting group of conserved TFBS (Tables 5.1A, 5.1B and

Figures 5.3A, 5.3B) in both human and murine promoters. Furthermore, we were able to

identify genes with conserved regulatory sequences in the promoter regions of human,

or murine, NRF2 and AP-1 as shown in Table 5.2. Indeed, microarray analyses in Nrf2-

deficient mice (Table 5.3) confirmed that genes identified as Nrf2-dependent, including

ATF-2, were coregulated with genes elicited from our AP-1 in vitro studies as well as

the comparative analyses of promoter regions of NRF2 and AP-1 using bioinformatic

analyses. It has been noted [199] that a majority of prostate cancers contain fusion

genes that result in regulation via ETS family transcription factors. Our in silico results

(Tables 5.1A, 5.1B and 5.2) as well as our in vitro data (Figure 5.2) also demonstrate a

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role for ETS family members including ELK-1 which is, thus, in consonance with

previous reports.

The identification of conserved TFBS for pro-survival transcription factor NFkB in the

promoter regions of NRF2 and AP-1 (Tables 5.1A, 5.1B, 5.2 and Figures 5.3A, 5.3B)

raises an important question as to whether there could be any possible crosstalk between

NRF2, AP-1 and NFkB in concert that may contribute to the overall effects of the

SFN+EGCG combination in prostate cancer. Indeed, further studies would be necessary

to explore this possibility in greater detail. The identification of several key MAPK

genes in our microarray studies (Table 5.3) as NRF2-dependent is in congruence with

the known role(s) of MAPKs in NRF2 phosphorylation and activation. Besides, the

downregulation of NRF3, a negative regulator of ARE-mediated gene expression [200]

with substantial homology to NRF2, in our microarray data (Table 5.3) reinforces the

putative chemopreventive potential of the SFN+EGCG combination. Furthermore, the

elucidation of several coactivators and corepressors as NRF2-dependent including

nuclear receptor coactivators 1 and 7 (Ncoa1 and Ncoa7) and nuclear receptor

corepressor 1 (Ncor1) indicate that these cofactors may have a potentially significant

role to play in the ability of NRF2 to crosstalk with AP-1 in vivo.

We have demonstrated recently [194] that a combination of SFN+EGCG resulted in a

synergistic transactivation of the AP-1 reporter in HT-29 human colon carcinoma cells

that was mediated by histone deacetylases, inhibition of cellular senescence and SOD

signaling. Surprisingly, our current results show that the same combination of

SFN+EGCG results in a downregulation of the AP-1 reporter as well as key genes in

prostate cancer. Indeed, this leads to the intriguing possibility that dietary

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chemopreventive agents such as SFN+EGCG, when used in combination, may exert

different multifactorial mechanisms in different forms of cancer. Our current

transcriptional regulation study addresses this question in terms of providing a

discursive framework for understanding putative crosstalk between NRF2 and AP-1 in

prostate cancer that may potentially explain the behavior of the combination of

SFN+EGCG that we have observed in the current study. It is tempting to speculate that,

when extrapolated to a clinical setting, one would have to face the additional

confounding factor of idiosyncratic behavior in patient sub-populations. However, it

appears a priori that the combination of SFN+EGCG may hold promise for patients

with early-grade prostate cancer via concerted modulation of NRF2 and AP-1 pathways.

Further studies focusing on the specific signaling intermediates, as well as clinical

studies, would be necessary to better appreciate the putative chemopreventive efficacy

of the combination of SFN+EGCG in the management of prostate cancer.


Sujit Nair is extremely grateful to Dr. Li Cai for his patient training, expertise and

extensive help with the bioinformatic approaches used in this study. Sujit Nair also

thanks Avantika Barve, Tin-Oo Khor, Guoxiang Shen and Wen Lin for their help with

animal handling. This work was supported in part by RO1-CA073674, RO1-CA092515,

R01-CA118947 and RO1-CA094828 to Ah-Ng Tony Kong from the National Institutes

of Health (NIH).

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Figure 5.1A. Diminished transactivation of AP-1 luciferase reporter by

combinations of SFN and EGCG

PC-3 AP-1 cells were seeded in six-well plates and treated with individual dietary

factors, or with combinations of SFN and EGCG, as indicated. The AP-1 luciferase

activity was measured relative to vehicle control (DMSO) after 24 hours of incubation

and normalized against protein concentration. Values represent mean ± standard

deviation for three replicates, and are representative of seven independent experiments.

* P < 0.05, significantly different from vehicle control (Ctrl) ; # P < 0.05, significantly

different from each other.

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Figure 5.1B. Viability of the PC-3 AP-1 cells with the combinations of SFN and

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PC-3 AP-1 cells were treated with individual dietary factors or with combinations of

SFN and EGCG for 24 hr or 48 hr, as indicated, and treated with MTS assay reagent to

ascertain cell viability. Values represent mean ± standard deviation for six replicates,


different from control.

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Figure 5.2. Temporal gene expression profiles elicited by combinations of SFN and

EGCG

PC-3 AP-1 cells were treated for 6 hr or 10 hr with individual dietary factors, or

combinations of SFN and EGCG, as indicated. RNA was extracted, transcribed into

cDNA after ascertaining RNA integrity, and quantitative real-time PCR assays were

performed for twelve genes (i- xii, above) at both time-points, as indicated, using beta-

actin as the housekeeping gene. Values represent mean ± standard deviation for three

replicates of each gene, and are representative of two independent experiments.

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Figure 5.3.A.

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Figure 5.3.B.

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Figure 5.3. Conserved Transcription Factor Binding Sites (TFBS) in promoter

regions of ATF-2 and ELK-1

Human promoter sequences of ATF-2 and ELK-1, or corresponding murine promoter

sequences, were retrieved from Gene2Promoter (Genomatix). Comparative promoter

analyses were then performed by input of these sequences in FASTA format into

MatInspector using optimized default matrix similarity thresholds. The similar and/or

functionally related TFBS were grouped into ‘matrix families’ and graphical

representations of common TFBS were generated (Figure 5.3A, human ATF-2 and

ELK-1; Figure 5.3B, murine Atf-2 and Elk-1). The ‘V$’ prefixes to the individual

matrices are representative of the Vertebrate MatInspector matrix library.

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CHAPTER 6

Regulatory potential for concerted modulation of Nrf2- and Nfkb1-mediated gene

expression in inflammation and carcinogenesis16,17,18.

6.1. Abstract

Many studies have implicated Nrf2 (Nfe2l2) in cancer and inflammation-associated

diseases such as colitis or inflammatory bowel disease; and Nfkb1 in inflammation and

cancer. However, despite a growing recognition of the important role(s) played by Nrf2

and Nfkb1, the regulatory potential for crosstalk between these two important

transcription factors in inflammation and carcinogenesis has not been explored. To

delineate conserved transcription factor binding sites (TFBS) signatures, we performed

in silico bioinformatic analyses on the promoter regions of human and murine Nrf2 and

Nfkb1, as well as coregulated genes. In order to investigate conserved biological

features, we performed multiple sequence alignment of Nrf2 and Nfkb1 genes in five

mammalian species – human, chimpanzee, dog, mouse and rat. We identified key

regulatory genes in distinct inflammation/cancer signatures and constructed a canonical

regulatory network for concerted modulation of Nrf2 and Nfkb1 involving several

16Work described in this chapter is under consideration for publication as Nair, S., Kong, AN., Cai, L. 17Keywords : Nrf2, Nfkb1, regulatory network, inflammation, carcinogenesis 18Abbreviations : BHA, butylated hydroxyanisole; EGCG, epigallocatechin-3-gallate; MAPK, mitogen-activated protein kinase; Nrf2 (Nfe2l2), Nuclear Factor-E2-related factor 2; Nfkb1, Nuclear Factor-κB1; NCSRS, Non-Coding Sequence Retrieval System; SFN, sulforaphane; TFBS, Transcription Factor Binding Sites.

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members of the mitogen-activated protein kinase (MAPK) family. The identification of

conserved TFBS and biological features across mammalian species, along with

coregulation of several MAPKs with Nrf2 and Nfkb1, underscore putative crosstalk

between these two critical transcription factors modulated via the MAPK cascade that

may influence inflammation-associated etiopathogenesis of cancer. We present a

canonical model for concerted modulation of Nrf2 and Nfkb1 in

inflammation/carcinogenesis. Taken together, the elucidation of potential relationships

among these two pivotal transcription factors may help to better understand

transcriptional regulation, as well as transcription factor networks, associated with the

etiopathogenesis of inflammation and cancer.

6.2. Introduction

The National Cancer Institute (NCI) Inflammation and Cancer Think Tank in Cancer

Biology [201] has recognized that epidemiologic and clinical research corroborates an

increased risk of certain cancers in the setting of chronic inflammation. Indeed, chronic

inflammation, because of both infectious and non-infectious etiologies, has been

associated with an increased risk of cancer development at a number of organ sites, with

infectious agents estimated to be responsible for the development of 18% of all new

cancer cases worldwide [202]. Infectious agents linked to cancer include Hepatitis B

virus and liver cancer, Helicobacter pylori and stomach cancer, and liver fluke infection

and cholangiocarcinoma. Additionally, a number of inflammatory conditions without an

infectious etiology result in a significantly increased cancer risk. Examples include

chronic gastroesophageal reflux-induced esophageal cancer, proliferative inflammatory

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atrophy-induced prostate cancer and chronic ulcerative colitis-associated colorectal

cancer. Activation of inflammatory cells is accompanied by an increase in the release of

reactive oxygen species (ROS) at the site of inflammation. Excess levels of ROS, due to

chronic inflammation, may contribute to carcinogenesis by reacting with DNA to form

oxidative DNA adducts possibly leading to mutagenesis and impaired regulation of

cellular growth [202]. Many of the processes involved in inflammation (e.g., leukocyte

migration, dilatation of local vasculature with increased permeability and blood flow,

angiogenesis), when found in association with tumors, are more likely to contribute to

tumor growth, progression, and metastasis than to elicit an effective host anti-tumor

response [201]. Recently, it has been shown [203] that signals downstream of the

receptor for advanced glycation end-products (RAGE) can fuel chronic inflammation,

creating a microenvironment that is ideal for tumor formation in a mouse model of skin

cancer.

Nuclear Factor-E2-related factor 2 (Nrf2 or Nfe2l2) is indispensable to cellular defense

against many chemical insults of endogenous and exogenous origin, which play major

roles in the etiopathogenesis of many cancers and inflammation-related diseases such as

inflammatory bowel disease, and Parkinson’s disease [149]. Under basal conditions,

Nrf2 - a member of the Cap-N-Collar family of transcription factors - is sequestered in

the cytoplasm by Keap1 resulting in enhanced proteasomal degradation of Nrf2. In

conditions of oxidative stress, Nrf2 is released from Keap1 either by direct oxidative

modification of Keap1 or after phosphorylation by redox sensitive protein kinases,

translocates to the nucleus and, in combination with other transcription factors, activates

transcription of genes containing an antioxidant response element (ARE) in their

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promoter regions resulting in a cytoprotective adaptive response. This adaptive response

is characterized by upregulation of a battery of antioxidative enzymes and decreased

sensitivity to oxidative damage and cytotoxicity. These antioxidative enzymes have also

been shown to attenuate inflammatory damage and neutralize ROS implicated in

inflammatory signaling pathways. We have also reported [177] that Nrf2 could play an

important role in protecting intestinal integrity, through regulation of proinflammatory

cytokines and induction of phase II detoxifying enzymes. Besides, we have

demonstrated [204] that mitogen activated protein kinase (MAPK) pathways such as

extracellular signal-regulated kinase (ERK) and c-jun N-terminal kinase (JNK)

signaling pathways played important and positive roles in chemopreventive agent

butylated hydroxyanisole (BHA)-induced and Nrf2-dependent regulation of ARE-

mediated gene expression, as well as the nuclear translocation of Nrf2 in HepG2 cells.

We have observed previously [205] that activation of MAPK pathways induces ARE-

mediated gene expression via a Nrf2-dependent mechanism. In addition, we have also

shown [41] that different segments of the Nrf2 transactivation domain have different

transactivation potential and that different MAPK have differential effects on Nrf2

transcriptional activity with ERK and JNK pathways playing an unequivocal role in the

positive regulation of Nrf2 transactivation domain activity [41,149].

Importantly, recent mouse studies provide strong and direct genetic evidence that the

classical, IKK- (inhibitor-of-NF B kinase- )-dependent Nuclear Factor-κB (NF- B)-

activation pathway, which was proposed several years ago to be the molecular link

between inflammation and carcinogenesis, is a crucial mediator of tumour promotion

[206]. Indeed, several pro-inflammatory cytokines and chemokines — such as TNF, IL-

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1, IL-6 and CXC-chemokine ligand 8 (CXCL8; also known as IL-8), all of which are

encoded by target genes of the IKK- -dependent NF- B-activation pathway — are

associated with tumour development and progression in humans and mice. It has, thus,

been hypothesized that activation of NF- B by the classical, IKK- -dependent pathway

is a crucial mediator of inflammation-induced tumour growth and progression, as well

as an important modulator of tumour surveillance and rejection [206]. We have also

demonstrated [80] that the suppression of NF- B and NF- B-regulated gene expression

(VEGF, cyclin D1 and Bcl-XL) by chemopreventive isothiocyanates sulforaphane

(SFN) and phenethyl isothiocyanate (PEITC) is mainly mediated through the inhibition

of IKK phosphorylation, particularly IKK-β, and the inhibition of IқB-α

phosphorylation and degradation, as well as the decrease of nuclear translocation of p65

in human prostate cancer PC-3 cells. Recently, it has been observed [207] that PEITC

suppresses receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis by

blocking activation of ERK1/2 and p38 MAPK in RAW264.7 macrophages. Besides,

HL60 cells treated with fisetin presented high expression of NFkappaB, activation of

p38 MAPK and an increase of phosphoprotein levels [208].

In this study, we explored the potential for putative crosstalk between Nrf2 and NF-κB

signaling pathways in inflammation/injury and carcinogenesis. We performed in silico

bioinformatic analyses to delineate conserved transcription factor binding sites (TFBS)

or regulatory motifs in the promoter regions of human and murine Nrf2 and Nfkb1, as

well as coregulated genes. We performed multiple sequence alignment of Nrf2 and

Nfkb1 genes in five mammalian species and studied conserved biological features. We

also looked at microarray data from public repositories such as Oncomine [209], Gene

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=3551�

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=3569



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Expression Omnibus (GEO), Public Expression Profiling Resource (PEPR) as well as

datasets from the Kong Laboratory, in order to dissect the role(s) of key regulatory

genes in these select inflammation/cancer signatures and constructed a regulatory

network for concerted modulation of Nrf2 and Nfkb1 involving several members of the

MAPK family. Our in silico analyses show that concerted modulation of Nrf2 and NF-

κB signaling pathways, and putative crosstalk involving multiple members of the

MAPK family, may be potential molecular events governing inflammation and

carcinogenesis.


Identification of microarray datasets bearing inflammation/injury or

cancer signatures : We perused several microarray datasets from public repositories

such as Oncomine, GEO, PEPR, as well as datasets from the Kong Laboratory. We

selected thirteen datasets that presented distinct signatures of inflammation or injury or

carcinogenesis. Specifically, these studies reflected data on prostate cancer, spinal

trauma, inflammatory response to injury; and genes modulated by chemopreventive

agents/toxicants in Nrf2-deficient animal models. These studies encompassed three

mammalian species – human, mouse and rat – and exhibited modulation of both Nrf2

(Nfe2l2) and Nfkb1 genes as well as coregulated genes.

Promoter Analyses for Transcription Factor Binding Sites (TFBS) : The

promoter analyses were performed in the laboratory of Dr. Li Cai (Department of

Biomedical Engineering, Rutgers University) using Genomatix MatInspector [195,196].

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Briefly, human promoter sequences of NFE2L2 and NFKB1, or corresponding murine

promoter sequences, were retrieved from Gene2Promoter (Genomatix). Comparative

promoter analyses were then performed by input of these sequences in FASTA format

into MatInspector using optimized default matrix similarity thresholds. The similar

and/or functionally related TFBS were grouped into ‘matrix families’ and graphical

representations of common TFBS were generated. The ‘V$’ prefixes to the individual

matrices are representative of the Vertebrate MatInspector matrix library. Similarly, we

also elucidated common TFBS amongst the three topmost conserved human regulatory

sequences after multiple alignment (as described below) of NRF2 and NFKB1

sequences.

Multiple species alignment of Nrf2 (Nfe2l2) and Nfkb1 sequences : Non-

coding sequences of Nfe2l2 and Nfkb1 genes in five mammalian species – human,

chimpanzee, dog, mouse, and rat – were retrieved using the Non-Coding Sequence

Retrieval System (NCSRS) for comparative genomic analysis of gene regulatory

elements that has been previously developed and published [210] by the Cai

Laboratory, and is readily available at http://cell.rutgers.edu/ncsrs/. Multiple sequence

alignment was performed by submitting the non-coding sequences to MLAGAN (Multi-

LAGAN, Multi-Limited Area Global Alignment of Nucleotides) [211] that is

compatible with the VISTA visualization tool. The MLAGAN alignments were, thus,

visualized using VISTA by projecting them to pairwise alignments with respect to one

reference sequence (human) as baseline. The common TFBS between Nfe2l2 and

Nfkb1 between the top biological features that were conserved in multiple species were

http://cell.rutgers.edu/ncsrs/

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then determined using Genomatix MatInspector as described earlier in Materials and

Methods under Promoter Analyses for TFBS.

Construction and validation of canonical first-generation regulatory

network involving Nrf2 (Nfe2l2) and Nfkb1 : A putative regulatory network for Nrf2

(Nfe2l2) and Nfkb1 representing 59 nodes and 253 potential interactions was

constructed using Cytoscape 2.5.2 software [212]. Further, we validated our network

using PubGene [213], a literature network where connections are a strong indicator of

biological interaction. Additional validation was achieved by the generation of a

biological network with these gene identifiers through the use of Ingenuity Pathways

Analysis (Ingenuity® Systems, www.ingenuity.com). For this purpose, a dataset

containing gene identifiers was uploaded into the application. Each gene identifier was

mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base.

These genes, called focus genes, were overlaid onto a global molecular network

developed from information contained in the Ingenuity Pathways Knowledge Base.

Networks of these focus genes were then algorithmically generated based on their

connectivity.

Putative model for Nrf2-Nfkb1 interactions in inflammation and

carcinogenesis : A pictorial model for Nrf2-Nfkb1 interactions was generated using

Pathway Builder Tool 2.0 available from Protein Lounge, San Diego, CA.

http://www.ingenuity.com/

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6.4. Results

6.4.1. Identification of microarray datasets bearing inflammation/injury or cancer

signatures

In order to investigate distinct signatures of inflammation/ injury or carcinogenesis, we

perused several microarray datasets from public repositories such as Oncomine, GEO,

PEPR, as well as microarray datasets from the Kong Laboratory. As summarized in

Table 6.1, we selected thirteen datasets that presented distinct signatures of

inflammation/injury or carcinogenesis. Specifically, these studies reflected data on

prostate cancer, spinal trauma, inflammatory response to injury; and genes modulated

by chemopreventive agents/toxicants in Nrf2-deficient animal models. These studies

encompassed three mammalian species – human, mouse and rat – and exhibited

modulation of both Nrf2 (or Nrf2-dependent) and Nfkb1 genes as well as coregulated

genes. In other words, all these studies presented modulation of both Nrf2 (Nfe2l2) and

Nfkb1 genes in concert, except for the studies with Nrf2-deficient animal models where

Nrf2-dependent genes were elucidated. Interestingly, these datasets exhibited

modulation of several key members of the MAPK family as well as cofactors of Nrf2

and Nfkb1. This literature pre-screen encouraged us to investigate further the regulatory

potential for concerted modulation of Nrf2- and Nfkb1-mediated gene expression in

inflammation and carcinogenesis using an in silico bioinformatic approach.

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6.4.2. Comparative promoter analyses of Nrf2 (Nfe2l2) and Nfkb1 for conserved

Transcription Factor Binding Sites (TFBS)

To identify conserved TFBS signatures, we performed comparative analyses of Nrf2

and Nfkb1 murine promoter sequences (Figure 6.1A) using Genomatix MatInspector

[195,196] as described in Materials and Methods. We also studied NRF2 and NFKB1

human promoter sequences (Figure 6.1B) similarly. Table 6.2 includes, as indicated, an

alphabetical listing of the conserved vertebrate (V$) matrix families between these two

transcription factors. The major human matrix families included Activator protein 4 and

related proteins, Ccaat/Enhancer Binding Protein, Camp-responsive element binding

proteins, E2F-myc activator/cell cycle regulator, E-box binding factors, Basic and

erythroid krueppel like factors, Fork head domain factors, Myc associated zinc fingers,

Nuclear receptor subfamily 2 factors, p53 tumor suppressor, RXR heterodimer binding

sites, SOX/SRY-sex/testis determining and related HMG box factors, Signal transducer

and activator of transcription, X-box binding factors, Zinc binding protein factors, and

Two-handed zinc finger homeodomain transcription factors, amongst others. Some key

conserved murine matrix families included AHR-arnt heterodimers and AHR-related

factors, Activator protein 2, Activator protein 4 and related proteins, E2F-myc

activator/cell cycle regulator, E-box binding factors, Basic and erythroid krueppel like

factors, Farnesoid X - activated receptor response elements, Heat shock factors, Ikaros

zinc finger family, Myc associated zinc fingers, Nuclear factor kappa B/c-rel, Nuclear

receptor subfamily 2 factors, Nuclear respiratory factor 1, p53 tumor suppressor,

Pleomorphic adenoma gene, RXR heterodimer binding sites, SOX/SRY-sex/testis

determining and related HMG box factors, Serum response element binding factor,

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Tata-binding protein factor, Zinc binding protein factors, amongst others. Indeed, as

evident from Table 6.2, several matrix families were conserved between Nrf2 and

Nfkb1 in both human and murine promoters.

6.4.3. Multiple species alignment of Nrf2 (Nfe2l2) and Nfkb1 sequences

With the objective of investigating conserved biological features across different

mammalian species, we performed multiple species alignment of Nrf2 (Nfe2l2) and

Nfkb1 sequences as described in Materials and Methods. We used the Non-Coding

Sequence Retrieval System (NCSRS) for comparative genomic analysis of gene

regulatory elements that has been previously developed and published [210] by the Cai

Laboratory, and is readily available at http://cell.rutgers.edu/ncsrs/ and retrieved non-

coding sequences of Nfe2l2 and Nfkb1 genes in five mammalian species – human,

chimpanzee, dog, mouse, and rat. As shown in Figures 6.2A and 6.2B, we performed

multiple sequence alignment using MLAGAN (Multi-LAGAN, Multi-Limited Area

Global Alignment of Nucleotides) [211] for Nfe2l2 and Nfkb1 genes respectively, with

respect to one reference sequence (human) as baseline. The phylogenetic tree for Nfe2l2

and Nfkb1 in the five species under consideration was constructed (Figure 6.2C). The

conserved biological features across species for each of Nfe2l2 and Nfkb1 genes were

perused, and the top five features for Nfe2l2 and the top three features for Nfkb1, as

numbered in Figures 6.2A and 6.2B, are listed in Tables 6.3A and 6.3B respectively.

Sequence 4 for Nfe2l2 and Sequence 1 for Nfkb1 exhibited the highest degree of

conservation across species at 98.86% and 86.58% respectively. In addition, the top

three conserved sequences in the human sequences of both these genes (Sequences 4, 3,

http://cell.rutgers.edu/ncsrs/

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and 2 for Nfe2l2 in that order, and Sequences 1, 2, and 3 for Nfkb1 in that order) as

evident from Tables 6.3A and 6.3B were submitted to Genomatix MatInspector. The

common TFBS between Nfe2l2 and Nfkb1 between these biological features that were

conserved in multiple species were then determined (Figure 2D) and tabulated along

with the other TFBS results for comparative promoter analyses in Table 6.2 discussed

earlier.

6.4.4. Construction and validation of canonical first-generation regulatory network

involving Nrf2 (Nfe2l2) and Nfkb1

In order to construct a canonical first-generation biological network for Nrf2-Nfkb1

interactions, we streamlined our study to five datasets summarized in Table 6.4 which

were representative of the most distinct inflammation/injury and cancer signatures from

the thirteen datasets perused earlier. We obtained gene expression values for 59 genes,

as shown in Table 6.4, including Nrf2 (Nfe2l2), Nfkb1, several cofactors, and many

members of the MAPK family from these datasets. As shown in Figure 6.3A, we

constructed a putative first-generation regulatory network for Nrf2 (Nfe2l2) and Nfkb1

representing 59 nodes and 253 potential interactions using Cytoscape 2.5.2 software

[212]. Further, we validated our network by submitting 20 representative genes from

Table 6.4 to PubGene [213], a literature network where connections are a strong

indicator of biological interaction, and retrieved biological networks in human (Figure

6.3B) and mouse (Figure 6.3C), thus delineating gene signatures that validated the

putative biological role(s) of the genes elucidated in the current in silico study that

served as the source and target nodes in our regulatory network. We further validated

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our network by querying Ingenuity Pathways Analysis (Ingenuity Systems®,

www.ingenuity.com) application with the 59 gene identifiers forming the basis of our

network, and obtained a biological network (Figure 6.3D) with a high degree of

functional crosstalk based on the Ingenuity Knowledge Base reiterating the potential for

crosstalk between multiple members of the MAPK family that might modulate the

Nrf2-Nfkb1 interactions as indicated in our canonical network (Figure 6.3A).

6.4.5. Putative model for Nrf2-Nfkb1 interactions in inflammation and

carcinogenesis

Based on the extensive experience [40,50,149,204,205,214] of the Kong Laboratory

with Nrf2-Keap1 pathway and role(s) of MAPK/dietary chemopreventives/toxicants,

our many microarray studies [52,55,56,58,215-218] in Nrf2-deficient mice, our studies

[16,80] on NF- B pathway and chemopreventive agents, and the gene signatures

elicited in inflammation and carcinogenesis in the current in silico study using our data

as well as publicly-available data from other research groups as indicated earlier, we

generated a pictorial model (Figure 6.4) for Nrf2-Nfkb1 interactions using Pathway

Builder Tool 2.0 available from Protein Lounge, San Diego, CA. In essence, chemical

signals generated by dietary chemopreventive agents or toxicants, or inflammatory

signals, may cause Nrf2 nuclear translocation that sets in motion a dynamic machinery

of co-activators and co-repressors that may form a multi-molecular complex with Nrf2

to modulate transcriptional response through the antioxidant response element, ARE.

Inflammation may also cause release of Nfκb1 from IκB and stimulate Nfκb1 nuclear

translocation to modulate transcriptional response through the Nfκb1 response element,

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Nfκb-RE, along with cofactors of Nfκb1. Several members of the MAPK family may

act in concert with Nrf2 and Nfkb1 with multiple interactions between the members of

the putative complex to elicit the chemopreventive and pharmacotoxicological events in

inflammation and carcinogenesis.

6.5. Discussion

Karin and Greten [206] succinctly noted that carcinogenesis may be divided into three

mechanistic phases: initiation (which involves stable genomic alterations), promotion

(which involves the proliferation of genetically altered cells) and progression (which

involves an increase in the size of the tumor, the spreading of the tumor and the

acquisition of additional genetic changes). In 1863, Rudolf Virchow observed

leucocytes in neoplastic tissues and suggested [219] that the “lymphoreticular infiltrate”

reflected the origin of cancer at sites of chronic inflammation. Besides, persistent and

recurrent episodes of inflammation [220] mediated by aberrant activation of innate and

acquired immunity characterize a wide spectrum of idiopathic and infectious chronic

inflammatory disorders. In response to tissue injury [221], a multifactorial network of

chemical signals initiate and maintain a host response designed to 'heal' the afflicted

tissue involving activation and directed migration of leukocytes (neutrophils, monocytes

and eosinophils) from the venous system to sites of damage, and tissue mast cells also

play a significant role. Interestingly, inflammation and innate immunity most commonly

exert pro-tumorigenic effects [206] mediated through different types of leukocytes,

including normal tissue macrophages, tumor-associated macrophages, dendritic cells,

neutrophils, mast cells and T cells, which are recruited to the tumor microenvironment

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through interactions with local stromal cells and malignant cells. These leukocytes

produce cytokines, and growth and angiogenic factors, as well as matrix-degrading

proteases (such as the matrix metalloproteinases MMP1, MMP3 and MMP9) and their

inhibitors which allow tumor cells to proliferate, invade and metastasize [206].

Although the causal relationship between inflammation, innate immunity and cancer is

more widely accepted, many of the molecular and cellular mechanisms mediating this

relationship remain unresolved [221]. Many studies have implicated Nrf2 (Nfe2l2) in

cancer [9,149,222-224] or inflammation-associated diseases such as colitis [177,202],

and Parkinson’s disease [225]; and NF- B in inflammation [226-228] and cancer

[80,229-231]. Indeed, the identification of combinatorial, or synergistic, transcription

factors and the elucidation of relationships among them are of great importance for

understanding transcriptional regulation as well as transcription factor networks [232].

However, despite a growing recognition of the important role(s) played by these two

pivotal transcription factors, the regulatory potential for crosstalk between these two

important transcription factors in inflammation and carcinogenesis has not been

explored.

The perusal of several microarray datasets from public repositories such as Oncomine,

GEO, PEPR, as well as datasets from the Kong Laboratory, facilitated the identification

of thirteen datasets (Table 6.1) presenting distinct signatures of inflammation/injury or

carcinogenesis that served as our literature pre-screen for concerted modulation of Nrf2

and Nfkb1 genes. The comparative analyses of TFBS in these two gene promoters

revealed that many matrix families were conserved between human NRF2 and NFKB1

promoters, and between murine Nrf2 and Nfkb1 promoter regions (Figure 6.1 and Table

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6.2). Furthermore, as elucidated in Table 6.2, several functionally important matrix

families were also found to be common across human and murine species, including

activator protein 4, E2F-myc activator/cell cycle regulators (V$E2FF), E box binding

factors, basic and erythroid krueppel like factors, p53 tumor suppressor, and RXR

heterodimer binding sites (V$RXRF), amongst others. The identification of V$E2FF is

significant because disruption of retinoblastoma protein (pRb), a key controller of E2F

activity and G1/S transition in the cell cycle, can alter the growth-inhibitory potential of

TGF-β in the inflammatory milieu of chronic liver disease and contribute to cancer

development [233]. Inflammatory conditions can enhance the genotoxic effects of

carcinogenic polycyclic aromatic hydrocarbons such as benzo[a]pyrene (BaP) through

upregulation of CYP1B1 expression associated with increased phosphorylation of p53

tumor suppressor at Ser-15 residue, enhanced accumulation of cells in the S-phase of

the cell cycle and potentiation of BaP-induced apoptosis [234]. Thus, the presence of

conserved p53 TFBS in Nrf2/Nfkb1 promoters may point to a critical role for

inflammation in the etiopathogenesis of cancer, and underscore the relevance of

crosstalk between these two transcription factors. The identification of V$RXRF is

important since RXR physically interacts with peroxisome proliferator activated

receptor (PPAR-α), a major player in lipid metabolism and inflammation, and PPAR-α

agonists like fenofibrate inhibit NF-κB DNA-binding activity [235]. In addition, a

conserved TFBS for NF- B itself (Table 6.2) was found to be present in murine

promoter regions of Nrf2 and Nfkb1 strengthening the potential for crosstalk between

these two transcription factors. Our multiple sequence alignments (Figures 6.2A,B and

Tables 6.3A,B) enabled the study of conserved biological features for each gene across

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five mammalian species and the construction of a phylogenetic tree (Figure 6.2C).

Interestingly, several key biological features were elicited on subjecting the top three

conserved human sequences of each gene to comparative promoter analyses (Figure

6.2D and Table 6.2). Notably, autoimmune regulatory element binding factors

(V$AIRE) and PPAR (or V$PERO) were conserved in these promoters. Recently, a

cyclopentenonic prostaglandin 15-Deoxy-Delta(12,14)-prostaglandin J(2) has been

shown to inhibit tumor necrosis factor (TNF)-related apoptosis-inducing ligand

(TRAIL) mRNA expression by downregulating the activity of its promoter in T

lymphocytes with NFκB being identified as a direct target of this prostanoid that is also

regulated by activation of PPAR-γ [236]. The identification of PPAR in the promoter

regions of Nrf2 and Nfkb1 in the current study, thus, reinforces the significance of these

transcription factors and provides a possible mechanistic pathway for crosstalk in

inflammation and cancer. Interestingly, a recent study [237] has reported that treatment

of human brain astrocytes with double-stranded RNA induced interferon regulatory

factor 3 (IRF3) phosphorylation and nuclear translocation followed by activation of

signal transducer and activator of transcription 1 (STAT1) along with a concomitant

activation of NFκB and MAPK cascade members (p38, JNK and ERK). In this study,

we identified interferon regulatory factors (IRFF) and STAT as being conserved in Nrf2

and Nfkb1 promoters, as well as MAPK members in our regulatory network (Figure

6.3A), which agrees with mechanistic evidence from the brain astrocyte study. It has

been observed [238] that stimulation with pro-inflammatory cytokines of CD38, known

to be responsible for lung airway inflammation, rendered it insensitive to treatment with

glucocorticoids such as fluticasone, dexamethasone or budesonide by inhibiting steroid-

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induced glucocorticoid-responsive element (GRE)-dependent gene transcription. We

also identified conserved GRE in the Nrf2/Nfkb1 promoters that further validated our

results as biologically relevant. In addition, cell cycle regulators, heat shock factors, and

several other matrices, were found to be conserved between the two genes, thus,

underscoring the biological relevance, and the intrinsic complexity, of Nrf2/Nfkb1

crosstalk from a functional standpoint.

Further, we streamlined our study to five datasets (Table 6.4) which were representative

of the most distinct inflammation/injury and cancer signatures of interest and

constructed a canonical first-generation regulatory network (Figure 6.3A) for Nrf2

(Nfe2l2) and Nfkb1 representing 59 nodes and 253 potential interactions. We generated

functionally relevant PubGene literature networks in human (Figure 6.3B) and mouse

(Figure 6.3C), and using the Ingenuity Knowledge Base (Figure 6.3D), thus delineating

gene signatures that validated the biological role(s), and potential for crosstalk, of the

genes elucidated in the current in silico study. Our future work includes the expansion

of our current study objectives to generate more detailed second-generation or third-

generation regulatory networks for Nrf2 and Nfkb1 as more functional data emerges on

these gene targets of interest and their interactions with coactivator/corepressor modules

that associate with them. Interestingly, as shown in our current first-generation network

(Figure 6.3A), several MAPKs play a central role in mediating the transcriptional

effects of Nrf2 and Nfkb1. This is, indeed, in consonance with the known role of

MAPKs in potentiating Nrf2-mediated ARE-activation [41,204,205] and their role in

modulating NF- B [207,208], thus, further underscoring the biological applicability of

our results. Finally, we present a gestalt pictorial overview (Figure 6.4) of our current

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knowledge of concerted modulation of Nrf2 and Nfkb1 based on the data from this

study and our extensive experience in cancer chemoprevention.

The results from our current in silico study may strengthen the possibility that scientists

could, in the future, consider pursuing Nrf2, Nfkb1 and MAPKs as potential targets in

early drug discovery screens for the management of inflammation and cancer. In

contemporary times, systems biology has interfaced with the drug discovery process to

enable high-throughput screening of multiple drug targets and target-based leads.

Indeed, a combination of high-throughput screening, kinase-specific libraries and

structure-based drug design has facilitated the discovery of selective kinase inhibitors

[239]. Needless to add, the benefits of applying molecular profiling to drug discovery

and development include much lower failure rates at all stages of the drug development

pipeline, faster progression from discovery through to clinical trials and more successful

therapies for patient subgroups [240]. Thus, development of specific inhibitors that

might regulate the specific crosstalk between the two central pleiotropic transcription

factors Nrf2 and Nfkb1, and with the associated family of kinases, may serve as one of

many strategies that might aid in the drug discovery process. Taken together, our study

provides a canonical framework to understand the regulatory potential for concerted

modulation of Nrf2 and Nfkb1 in inflammation and cancer. Further studies addressing

this question with specific emphasis on cofactor modules binding to these transcription

factors and coregulation with upstream signaling molecules in the MAPK cascade will

enable a better appreciation of the emerging key role(s) of, and the crosstalk between,

these two transcription factors in inflammation and carcinogenesis.

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Sujit Nair is very grateful to Sung Tae Doh for his expert help with the multiple species

alignment, and to Dr. Li Cai for the gracious training on the bioinformatic approaches

used in this study. This work was supported in part by RO1 CA-094828 to Ah-Ng Tony

Kong from the National Institutes of Health (NIH).

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Figure 6.1. A.

178

Figure 6.1. B.

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Figure 6.1. Conserved TFBS between Nfe2l2 and Nfkb1

Vertebrate (V$) matrix families conserved between murine (or human) Nfe2l2 and

Nfkb1 promoter regions were identified using Genomatix MatInspector.

6.1. A. Conserved TFBS between murine Nfe2l2 and Nfkb1; 6.1.B. Conserved TFBS

between Human NFE2L2 and NFKB1.

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Figure 6.2. A.

181

Figure 6.2. B.

182

Figure 6.2. C.

183

Figure 6.2. D.

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Figure 6.2. Multiple species alignment

Non-coding sequences of Nfe2l2 and Nfkb1 genes in five mammalian species – human,

chimpanzee, dog, mouse, and rat – were retrieved using the Non-Coding Sequence

Retrieval System (NCSRS) for comparative genomic analysis of gene regulatory

elements. Multiple sequence alignment was performed by submitting the non-coding

sequences to MLAGAN, and visualized by projecting them to pairwise alignments with

respect to one reference sequence (human) as baseline [Pink regions, Conserved Non-

Coding Sequences (CNS); Dark blue regions, Exons]. The numbers indicate CNS that

were identified across species.

6.2. A. Multiple species alignment for Nfe2l2; 6.2. B. Multiple species alignment for

Nfkb1; 6.2. C. Phylogenetic Tree for Nfe2l2 and Nfkb1; 6.2. D. Conserved TFBS

between NFE2L2 and NFKB1 among top matching human sequences

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Figure 6.3. A.

186

Figure 6.3. B.

187

Figure 6.3. C.

188

Figure 6.3. D.

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Figure 6.3. Canonical regulatory networks for Nrf2-Nfkb1 interactions in

inflammation-associated carcinogenesis

6.3. A. A putative regulatory network for Nrf2 (Nfe2l2) and Nfkb1 representing 59

nodes and 253 potential interactions implicating several members of the MAPK family;

6.3. B. Literature network in human; 6.3. C. Literature network in mouse; 6.3. D.

Functional crosstalk in biological network of Nfe2l2, Nfkb1 and various members of

the MAPK cascade.

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Figure 6.4. Putative Model for Nrf2-Nfkb1 interactions in inflammation and

carcinogenesis

Chemical signals generated by dietary chemopreventive agents or toxicants, or

inflammatory signals, may cause Nrf2 nuclear translocation that sets in motion a

dynamic machinery of co-activators and co-repressors that may form a multi-molecular

complex with Nrf2 to modulate transcriptional response through the antioxidant

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response element, ARE. Inflammation may also cause release of NF-κB from IκB and

stimulate NF-κB nuclear translocation to modulate transcriptional response through the

NF-κB response element, NF-κB-RE, along with cofactors of NF-κB. Several members

of the MAPK family may act in concert with Nrf2 and Nfkb1 with multiple interactions

between the members of the putative complex to elicit the chemopreventive and

pharmacotoxicological events in inflammation and carcinogenesis.

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CHAPTER 7

Pharmacogenomic investigation of potential prognostic biomarkers in human

prostate cancer19,20,21

7.1. Abstract

Prostate cancer is the second leading cause of cancer deaths among men in the United

States, and the seventh leading cause of deaths overall for men. The objective of this

study was to identify candidate biomarkers for human prostate cancer that could be

important for chemopreventive or therapeutic intervention, thus resulting in a delay in

advancement to clinical diagnosis of cancer. The development of rational

chemopreventive strategies requires knowledge of the mechanisms of prostate

carcinogenesis and crosstalk between important signaling pathways that are relevant in

prostate cancer. We used microarray analyses to identify genes that are upregulated or

downregulated at least five-fold in human prostate cancer. We generated functionally

relevant signaling pathways that are important in prostate cancer through metabolomics

analyses. Further, we identified TFBS-association signatures in the regulatory elements

of representative biomarkers. The identification of key target hubs such as p38 MAPK,

PPAR gamma, ESR2, VIP, Rb1 and others in the signaling networks, and of key

19Work described in this chapter is under consideration for publication as Nair, S., Cai, L., Kong, AN. 20Keywords: Human prostate cancer, microarray, bioinformatics, biomarkers, TFBS. 21Abbreviations : MAPK, mitogen-activated protein kinase; NRF2, nuclear factor (erythroid-derived-2)-related factor-2; TFBS, Transcription Factor Binding Sites.

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transcription factors including NRF2 in the regulatory analysis, is a first step to

identifying key biomarkers in prostate cancer. Taken together, using our systems

biology approach, we were able to identify several candidate biomarkers and signaling

networks that could be important target hubs for the development of chemopreventive

strategies that target one or more of the identified biomarkers, alone or in combination,

in order to improve the efficacy of chemoprevention in prostate cancer and enhance

patient survival.

7.2. Introduction

The incidence of prostate cancer in the United States has increased by 1.1% per year

from 1995–2003 [130,172]. According to the Centers for Disease Control and

Prevention (CDC) [130], prostate cancer is the second leading cause of cancer deaths

among men in the United States, and the seventh leading cause of deaths overall for

men. Interestingly, the National Cancer Institute (NCI)’s Surveillance Epidemiology

and End Results (SEER) Statistics Fact Sheets [173] show that, based on rates from

2002–2004, 16.72% of men born today will be diagnosed with cancer of the prostate at

some time during their lifetime, i.e., 1 in 6 men in the United States are at a lifetime risk

of developing prostate cancer. In addition, evidence from studies examining the

association between adult body mass index (BMI) and the risk of prostate cancer is

inconclusive with several cohort studies suggesting a positive association whereas other

studies report no association, although there is some indication that the positive

association is limited to fatal or more aggressive tumors [241].

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Two different types of precursors of prostate cancer can be found in the prostate gland,

for example, dysplastic areas termed prostatic intraepithelial neoplasia (PIN) [242,243]

and proliferative inflammatory atrophy (PIA) lesions. PIA lesions can be found near

adenocarcinomas and they can merge with high-grade PIN areas [243,244]. The

detection of prostate cancer, recurrence, or metastatic disease is often determined using

the serum prostate specific antigen (PSA) test. PSA is a serine protease that is

synthesized by both normal and malignant epithelial cells of the human prostate. PSA

expressed by malignant cells, however, is released into the serum at an increased level,

which can be detected to diagnose and monitor prostate cancer [245]. In a recent study

[246], the PSA nadir while intermittently taking a testosterone-inactivating

pharmaceutical agent was determined to be the best predictor of prostate cancer-specific

mortality. Nevertheless, despite the considerable attention given to PSA as a screening

test for prostate cancer, it is needle biopsy, and not the PSA test result, that actually

establishes the diagnosis of prostate cancer [175]. Garmey et al [247] noted that

although hormone-based therapies generally result in rapid responses, virtually all

patients ultimately develop androgen-independent progressive disease, and it is among

these men with hormone-refractory prostate cancer that the role of chemotherapy

continues to be investigated. To date, three drugs (estramustine, mitoxantrone, and

docetaxel) have been approved by the US Food and Drug Administration (FDA) for

first-line chemotherapy in hormone-refractory prostate cancer [247], with other agents

and combinations now under evaluation in ongoing clinical trials.

Epidemiological research on prostate cancer risk in men throughout the world has

identified significant correlations between dietary habits and prostate cancer occurrence.

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These studies have served as a catalyst for exploration of the potential of dietary

substances to act as chemopreventive agents, the tested agents including green tea

catechins, lycopene, soy isoflavones, pomegranate phenolics, selenium, vitamins E and

D, curcumin and resveratrol [248]. Indeed, prostate cancer is an ideal candidate disease

for chemoprevention because it is typically diagnosed in the elderly population with a

relatively slower rate of growth and progression, and therefore, even a modest delay in

the development of cancer, achieved through pharmacologic or nutritional intervention,

could result in substantial reduction in the incidence of clinically detectable disease

[249]. The development of rational chemopreventive strategies requires knowledge of

the mechanisms of prostate carcinogenesis and identification of agents that interfere

with these mechanisms. Because of the long time period for prostate carcinogenesis and

the large size of the cohort required for an evaluable study, identification and

characterization of early intermediate biomarkers and their validation as surrogate

endpoints for cancer incidence are essential for chemopreventive agent development

[250] .

Using microarray technology, we compared the transcriptomic profiles in cancerous

human prostate with that in normal prostate human tissue. We generated lists of genes

that were greatly upregulated or downregulated (at least five-fold) in tumor as compared

to normal. We then applied bioinformatic techniques and metabolomics analysis to

indentify potential biomarkers that might be important in prostate cancer, as well as

functional biological networks that might be critical in the etiopathogenesis of prostate

cancer. The candidate biomarkers in the networks identified in this study could be

important targets for chemoprevention research in prostate cancer.

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Human prostate RNA : FirstChoice® Human Prostate Tumor/Normal

Adjacent Tissue RNA (Catalog No. AM7288) was procured from Ambion (Applied

Biosystems/Ambion, Austin, TX). The FirstChoice® RNA was total RNA isolated from

an individual tumor (7288T) and the normal adjacent tissue (NAT, 7288N).

Assessment of RNA Integrity : RNA integrity was assessed using

formaldehyde gels in 1X MOPS buffer and RNA concentration was determined by the

260/280 ratio on a DU 530 UV/Visible spectrophotometer (Beckman).

Microarray Sample Preparation and Hybridization : Affymetrix (Affymetrix,

Santa Clara, CA) human genome U133 Plus 2.0 array was used to probe the global gene

expression profiles in prostate RNA. The cDNA synthesis and biotin-labeling of cRNA,

hybridization and scanning of the arrays, were performed at CINJ Core Expression

Array Facility of Robert Wood Johnson Medical School (New Brunswick, NJ). Each

chip was hybridized with cRNA derived from either cancerous or normal prostate.

Briefly, double-stranded cDNA was synthesized from 5 µg of total RNA and labeled

using the ENZO BioArray RNA transcript labeling kit (Enzo Life Sciences,

Inc.,Farmingdale, NY, USA) to generate biotinylated cRNA. Biotin-labeled cRNA was

purified and fragmented randomly according to Affymetrix’s protocol. Two hundred

microliters of sample cocktail containing 15 μg of fragmented and biotin-labeled cRNA

was loaded onto each chip. Chips were hybridized at 45°C for 16 h and washed with

fluidics protocol EukGE-WS2v5 according to Affymetrix’s recommendation. At the

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completion of the fluidics protocol, the chips were placed into the Affymetrix GeneChip

Scanner where the intensity of the fluorescence for each feature was measured.

dChip Data Analyses : The CEL files created from each sample (chip) were

first imported into dChip software [197,198] for further data characterization. Briefly, a

gene information file with current annotations and functional gene ontology was

generated and the Affymetrix Chip Description File (CDF) was specified. The data

were then normalized in dChip and the expression value for each gene was determined

by calculating the average of differences in intensity (perfect match intensity minus

mismatch intensity) between its probe pairs. A list of genes that were modulated at least

five-fold in cancerous prostate as compared to normal prostate was generated.

Thereafter, Clustering and Enrichment Analysis was performed to obtain hierarchical

tree clustering diagrams that provided functional classification of Affymetrix Probe Set

IDs and gene descriptions.

Biomarker and Metabolomics Analyses : Functional analyses, biomarker

filtration, and metabolomics analyses were performed through the use of Ingenuity

Pathways Analysis (Ingenuity®

Systems, www.ingenuity.com). Starting with the list of

genes that were regulated at least five-fold in the dChip analyses described earlier, we

performed functional analyses by associating them with biological functions in the

Ingenuity Pathways Knowledge Base. Using Ingenuity’s Biomarker Filter, we

identified representative biomarkers that were greatly modulated (at least five-fold) in

cancerous prostate as compared to normal. The criteria used for the filter were disease

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state (cancer), tissue type (prostate gland) and species (human). The candidate

biomarkers were toggled to their subcellular localization. Further, we subjected these

candidate biomarkers to Ingenuity’s Metabolomics analysis and identified biologically

relevant signaling networks in prostate cancer. Briefly, a data set containing gene

identifiers (GenBank Accession IDs) and corresponding expression values (obtained

earlier from our dChip analyses) was uploaded into in the application. Each gene

identifier was mapped to its corresponding gene object in the Ingenuity Pathways

Knowledge Base. An expression value cut-off of 1.0 was set to identify all genes whose

expression was significantly differentially regulated. These genes, called focus genes,

were overlaid onto a global molecular network developed from information contained

in the Ingenuity Pathways Knowledge Base. Networks of these focus genes were then

algorithmically generated based on their connectivity.

Identification of TFBS-association signatures : The co-regulated potential

biomarkers were now additionally investigated for Transcription Factor Binding Site

(TFBS)-association signatures using NCBI’s DiRE (Distant Regulatory Elements of co-

regulated genes) at the DiRE server for the identification of distant regulatory elements

of coregulated genes (http://dire.dcode.org) and the top ten transcription factors were

identified. DiRE's unique feature is the detection of regulatory elements outside of

proximal promoter regions, as it takes advantage of the full gene locus to conduct the

search. Function-specific regulatory elements consisting of clusters of specifically-

associated TFBSs were predicted, and the association of individual transcription factors

with the biological function shared by the group of input genes were scored.

http://dire.dcode.org/

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7.4. Results

7.4.1. Genes modulated in human prostate cancer

In order to identify important gene clusters that are modulated in human prostate cancer,

we performed dChip analysis of our microarray data as described in detail in Materials

and Methods. 408 genes that were modulated (upregulated or downregulated) at least

five-fold in cancerous prostate (7288T) over normal adjacent tissue (7288N) were

identified. The clustering analysis revealed ten significant gene clusters as shown in

Figure 7.1 including cell adhesion, cell division and cell cycle, enzymatic activity,

extracellular region, kinases and phosphatases, proteinaceous extracellular matrix,

phosphate transport, regulation of transcription, transcription factor activity, and

transporters. Major members of these important gene clusters that were all modulated at

least five-fold in human prostate cancer are listed in Table 7.1 along with their

GenBank Accession IDs and fold change values.

7.4.2. Biomarker filtration

To further identify functionally relevant biomarkers in prostate cancer, we used a

biomarker filter as described earlier under Materials and Methods and identified 21

candidate biomarkers that passed the filter. We toggled these biomarkers to their

subcellular localization as shown in Figure 7.2.A. The biomarkers included (i) VIP

(vasoactive intestinal polypeptide, 24.3 fold); (ii) SFTPD (surfactant, pulmonary-

associated protein D, -9.13 fold); (iii) BMP5 (bone morphogenetic protein 5, 12.55

fold); (iv) SHBG (sex hormone-binding globulin, 16.9 fold); (v) DLL 1 (delta-like 1

(Drosophila), -7.42 fold); (vi) FGF18 (Fibroblast growth factor 18, -20.06 fold); (vii)

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GRIA1 (glutamate receptor, ionotropic, AMPA 1, 7.06 fold); (viii) TJP3 (tight junction

protein 3 (zona occludens 3), -11.64 fold); (ix) ACVR1B (activin A receptor, type IB,

10.45 fold); (x) CXADR (coxsackie virus and adenovirus receptor, 24.8 fold); (xi)

NUDT3 (Nudix (nucleoside diphosphate linked moiety X)-type motif 3, 9.42 fold); (xii)

MSC (musculin (activated B-cell factor-1), -14.48 fold); (xiii) MBD1 (methyl-CpG

binding domain protein 1, -7.48 fold); (xiv) NR1I2 (nuclear receptor subfamily 1, group

I, member 2, 8.62 fold); (xv) ESR2 (estrogen receptor 2 (ER beta), 9.12 fold); (xvi)

TOP2A (topoisomerase (DNA) II alpha 170kDa, 19.24 fold); (xvii) RGS12 (regulator

of G-protein signaling 12, 7.19 fold); (xviii) NR1H4 (nuclear receptor subfamily 1,

group H, member 4, 9.94 fold); (xix) TNNC2 (troponin C type 2 (fast), -22.55); (xx)

MYH14 (myosin, heavy chain 14, 7.49); and, (xxi) ASNS (Asparagine synthetase, 7.49

fold). We searched for crosstalk between these potential biomarkers and deciphered a

few connections as shown in Figure 7.2.B. All the above biomarkers were

representative of different gene clusters that we had elicited in our microarray study and

were considered as focal nodes for further analysis.

7.4.3. Metabolomics analyses

To generate biologically relevant signaling networks and to identify biomarker targets

that are relevant specifically in prostate cancer pathogenesis, we subjected the 21

representative biomarkers identified earlier to Metabolomics analyses as described in

Materials and Methods. Two simplified networks were derived from the Metabolomics

analysis for the human prostate tumor biomarkers (i) Cell Death, Cellular Growth and

Differentiation, and Cellular Development (Figure 7.3.A), and (ii) Gene Expression,

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Cancer, Cellular Growth and Proliferation (Figure 7.3.B). These networks included p38

MAP Kinase, other MAP Kinases, HNF signaling molecules, LDL, Ncoa1, PPAR

gamma, and estrogen receptor, amongst other important signaling molecules. In order

to appreciate the complexity of the signaling networks that might be relevant in prostate

cancer, we merged these two simplified networks as shown in Figure 7.3.C. We were

thus able to identify major “target hubs” through which a greater number of connections

traversed as is evident from these networks.

7.4.4. Identification of TFBS-association signatures

To investigate TFBS-association signatures in the promoter regions of the 21

representative biomarkers obtained after filtration, we submitted these gene sequences

to DiRE analyses as described in Materials and Methods. The top ten TFBS-association

signatures were identified with respect to occurrence and importance as shown in Figure

7.4. These TFBS included (i) SPZ1, spermatogenic leucine zipper (ii)TATA, TATA

box (iii)IRF7, interferon regulatory factor 7 (iv)TAL1, T-cell acute lymphocytic

leukemia 1 (v) OSF2, osteoblast-specific transcription factor (vi)NRF2, nuclear factor

(erythroid-derived-2)-related factor-2 (vii)HNF3, hepatocyte nuclear factor 3 (viii)TEF,

transcriptional enhancer factor (ix)TBP, TATA-binding protein, and (x)SEF1, SL3-3

enhancer factor 1.

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7.5. Discussion

The Prostate Cancer Prevention Trial (PCPT) demonstrated that finasteride reduces the

prevalence of prostate cancer by 24.8 % (risk reduction), however, cost-effectiveness

analyses recently showed [251] that its use may be cost-effective only in high-risk

populations when taking into consideration adjustments for the impact on quality of life.

Nevertheless, prostate cancer is highly indicated for chemopreventive intervention due

to its inherent characteristics [248] which include (i) frequent discovery of latent

carcinoma, even in countries with low incidences of clinical cancer; (ii) very long time

to clinically significant cancer; (iii) few patients under 50 years of age (primarily a

disease of elderly men); (iv) strong influences of environmental factors such as food;

(v) temporal effectiveness of androgen deprival therapy; and (vi) no effective

therapeutic approaches once hormone-refractory neoplasms have developed. In drug

discovery, attempts have been made to use functional genomics in target identification

and validation, lead selection and optimization, and in preclinical studies to predict

clinical outcome [252]. Knowledge of the genetics and genomics of drug metabolizing

enzymes (DMEs) allows us to better understand and predict enzyme regulation and its

effects on exogenous (pharmacokinetics) and endogenous pathways as well as

biochemical processes (pharmacology) [253]. The identification of new drug targets for

therapeutic or preventive intervention is, thus, of great importance and would be greatly

assisted by recent developments in systems biology research.

In the current study, we provide a proof-of-concept demonstration of identification of

human prostate cancer biomarkers by bioinformatic approaches. To identify relevant

genes that were highly upregulated or downregulated in cancerous prostate as compared

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to normal prostate, we performed microarray analyses and clustered the genes that were

modulated at least five-fold into various functionally relevant gene categories as shown

in Figure 7.1 and listed in Table 7.1. In order to identify important signaling networks in

prostate cancer, we initially restricted ourselves to 21 potential biomarkers that passed

our biomarker filter. These biomarkers were representative of the gene clusters

identified in our microarray study. We then performed metabolomics analyses with

these representative biomarkers and generated functional signaling networks that would

be relevant in prostate cancer.

From our network in Figure 7.3.A, it is evident that p38 MAPK (mitogen-activated

protein kinase) is a major target hub with several connections pointing to or leading

away from this molecule. We have shown previously [254] that p38 MAPK negatively

regulates the induction of phase II DMEs that detoxify carcinogens, thus the

involvement of p38 MAPK as a major target hub in our network is in consonance with

our previous findings on the role of p38 in cancer chemoprevention. The retinoblastoma

tumor suppressor protein (Rb), a critical mediator of cell cycle progression, is

functionally inactivated in the majority of human cancers, including prostatic

adenocarcinoma, with 25% to 50% of prostatic adenocarcinomas harboring aberrations

in Rb pathway. Thus, the identification of Rb1 in both our signaling networks in Figures

7.3.A and 7.3.B underscores its relevance as an important biomarker in prostate cancer.

Furthermore, we identified NCOA1 (nuclear receptor coactivator 1) in Figures 7.3.A

and 7.3.B which we have previously reported [52] as a potential NRF2-dependent

binding partner, and also identified MAP2K6 in Figure 7.3.A. Since MAPK cascades

have differential effects on NRF2 transactivation domain activity, and since NCOA1 is

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an NRF2-dependent cofactor, the role of NRF2 as an important biomarker for prostate

cancer must be considered. We also noted the presence of VIP (vasoactive intestinal

polypeptide) and HNF4A (hepatocyte nuclear factor 4A) as major target hubs in both

networks in Figures 7.3.A and 7.3.B. This is important because receptors for VIP and

the human epidermal growth factor family of tyrosine kinase receptors (HER) are potent

promoters of cell proliferation, survival, migration, adhesion and differentiation in

prostate cancer [255]. Recently, a far module was found to support constitutive

expression of CYP3A4 (cytochrome P450 3A4), that is involved in the metabolism of

more than 50% of drugs and other xenobiotics, with the far module (like the distal

module) being structurally clustered by a PXR response element (F-ER6) and elements

recognized by HNF-4A. Thus, the identification of HNF-4A may relate to a heightened

potential for CYP3A4-mediated metabolism of any interventive agent for prostate

cancer prevention that must be taken note of. Interestingly, combined therapy against

both growth factor and steroid receptor signaling appears very challenging in the era of

optimization of therapeutic effect, and is currently under clinical investigation

[256,257]. A very promising application of this concept is reported [258] in experiments

that combined a pure-antiestrogen (Faslodex) and a tyrosine kinase inhibitor targeted to

EGFR (Iressa), giving rise to an additive suppression of growth and VEGF secretion in

NIH-H23 NSCLC and MCF-7 cells, through MAPK inhibition [256,258]. The

identification of estrogen receptor (ESR2) in our signaling network (Figure 7.3.B)

underscores the importance of steroid receptor signaling in hormone-refractory prostate

cancer. We also identified TGF-beta in Figure 7.3.B that is in agreement with a recent

report [259] on the relevance of TGF-beta related signaling markers in breast and

205

prostate cancer patients with bone metastasis. To obtain a comprehensive overview of

crosstalk between major signaling networks that may be relevant in human prostate

cancer, we merged the generated networks as shown in Figure 7.3.C. Several major

target hubs including p38 MAPK, PPAR gamma, ESR2, VIP, Rb1, amongst others,

were identified as key regulatory points in prostate cancer. The development of

chemopreventive agents that would target these major signaling molecules alone or in

combination may be pursued to further the cause of prostate cancer prevention.

Furthermore, our data on TFBS-association signatures in the regulatory elements of the

representative biomarkers revealed interesting transcription factors as shown in Figure

7.4. Notably, the identification of NRF2 was in corroboration with the identification of

NRF2-dependent gene NCOA1 as well as members of the MAPK family, including p38

MAPK, MAP2K6 and others in our metabolomics analyses. This is also in agreement

with our previous reports [18,216] on the role of NRF2 in prostate cancer, thus

underscoring the validity of pursing NRF2 as an additional biomarker target for prostate

cancer chemopreventive intervention.

In summary, we have identified genes that are modulated at least five-fold in human

prostate cancer. We were also able to generate functionally relevant signaling pathways

that are important in prostate cancer through metabolomics analyses. We also identified

TFBS-association signatures in the regulatory elements of representative biomarkers.

The identification of key target hubs such as p38 MAPK, PPAR gamma, ESR2, VIP,

Rb1 and others in the signaling networks, and of key transcription factors including

NRF2 in the regulatory analysis, is a first step to identifying key biomarkers in prostate

cancer. Using our systems biology approach, the development of chemopreventive

206

strategies that target one or more of the identified biomarkers, alone or in combination,

would greatly reduce the time needed for development of new drugs, and eliminate false

leads, in the drug discovery paradigm for prostate cancer.


Sujit Nair is very grateful to Dr. Li Cai for his excellent training on the bioinformatics

approaches used in this study. This work was supported in part by RO1 CA 118947 to

Ah-Ng Tony Kong from the National Institutes of Health (NIH).

207

Figure 7.1. Major gene clusters modulated in human prostate cancer

Major gene clusters upregulated or downregulated at least five-fold in human prostate

cancer as compared to normal prostate were identified by dChip microarray analyses

and are shown here.

208

Figure 7.2. A.

209

Figure 7.2. B.

Figure 7.2. Candidate biomarkers in human prostate cancer

Genes that passed the Ingenuity® biomarker filter from the genes modulated at least

five-fold in the microarray analyses of human prostate cancer were toggled to their

subcellular localization and are shown here. 7.2. A. Candidate biomarkers; 7.2. B.

Candidate biomarkers and putative crosstalk between them.

210

Figure 7.3. A.

211

Figure 7.3. B.

212

Figure 7.3. C.

213

Figure 7.3. Functional biological networks in human prostate cancer

Functional biological networks in human prostate cancer were identified through

Ingenuity® metabolomics analyses of candidate biomarkers identified from the

biomarker filter, and connections with relevant signaling molecules were deciphered as

described in Materials and Methods. 7.3. A. Cell Death, Cellular Growth and

Differentiation, Cellular Development Network; 7.3. B. Gene Expression, Cancer,

Cellular Growth and Proliferation Network; 7.3. C. Merged Network.

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Figure 7. 4. Identification of TFBS-association signatures

TFBS-association signatures in the regulatory regions of the candidate biomarkers were

identified by DiRE analyses. The top ten transcription factors based on occurrence and

importance are shown here.

215

CHAPTER 8

Differential regulatory networks elicited in androgen-dependent, androgen-

and estrogen-dependent, and androgen-independent human prostate

cancer cell lines 22,23,24

8.1. Abstract

To understand the organization of the transcriptional networks that govern the

etiopathogenesis of prostate cancer, we investigated the transcriptional circuitry

controlling human prostate cancer in androgen-dependent 22Rv1 and MDA PCa 2b

cells, androgen- and estrogen-dependent LNCaP cells and androgen-independent DU

145 and PC-3 prostate cancer cell lines. We performed microarray analyses and used

pathway prediction to identify biologically relevant regulatory networks that manifest

differentially in the representative prostate cancer cell lines. We present graphical

representations of global topology and local network motifs describing network

structures as well as sub-network structures for these conditional parameters (hormone

dependence) that might be important in carcinogenesis in the prostate gland. The large

number of target hubs identified in the current study including AP-1, NF-қB, EGFR,

ERK1/2, JNK, p38 MAPK, TGF beta, VEGF, PDGF, CD44, Akt, PI3K, NOTCH1,

22Work described in this chapter is under consideration for publication as Nair, S., Cai, L., Kong, AN. 23Keywords : Target hub, regulatory network, prostate cancer, biomarker 24Abbreviations : HRPC, Hormone Refractory Prostate Cancer; MAPK, Mitogen-activated Protein Kinase; AR, Androgen Receptor; TFBS, Transcription Factor Binding Sites.

216

CASP1, MMP2, and androgen receptor underscore the complexity of cellular events

including autoregulation, feedback loops and cross-talk that might govern progression

from early lesion to clinical diagnosis of prostate cancer, or metastatic potential of pre-

existent high-grade prostate intraepithelial neoplasia (HG-PIN) and advancement to

hormone refractory prostate cancer (HRPC). The identification of TFBS-association

signatures for TCF/LEF, SOX9 and ELK1 in the regulatory elements of the critical

biomarkers identified in this study provide additional prognostic biomarkers for

development of novel chemopreventive and therapeutic strategies in prostate cancer.

Taken together, the elucidation in this study of several target hubs in the latent structure

of biological networks generated in hormone-dependent and hormone-independent cell

lines, and the delineation of key TFBS-association signatures, would enhance our

understanding of regulatory nodes that might be central to identifying key players in

cellular signal transduction cascades that are important in the pathogenesis of prostate

cancer.

8.2. Introduction

Prostate cancer is an ideal candidate disease for chemoprevention because it is typically

diagnosed in the elderly population with a relatively slower rate of growth and

progression, and therefore, even a modest delay in the development of cancer, achieved

through pharmacologic or nutritional intervention, could result in substantial reduction

in the incidence of clinically detectable disease [249]. The development of rational

chemopreventive strategies requires knowledge of the mechanisms of prostate

carcinogenesis and identification of agents that interfere with these mechanisms.

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Because of the long time period for prostate carcinogenesis and the large size of the

cohort required for an evaluable study, identification and characterization of early

intermediate biomarkers and their validation as surrogate endpoints for cancer incidence

are essential for chemopreventive agent development [250]. One in six men in the

United States are at a lifetime risk of developing prostate cancer according to NCI’s

SEER statistics [173]. Although hormone-based therapies generally result in rapid

responses, virtually all patients ultimately develop androgen-independent progressive

disease, and it is among these men with hormone-refractory prostate cancer (HRPC)

that the role of chemotherapy continues to be investigated with three drugs

(estramustine, mitoxantrone, and docetaxel) being approved by the US Food and Drug

Administration (FDA) for first-line chemotherapy [247]. The identification of new

targets would greatly aid the progress in chemoprevention efforts and delay the clinical

diagnosis of cancer, translating into better survival and quality of life for patients.

Advances in diagnostic imaging have resulted in the development of fully automated

computer-aided detection systems for detecting prostatic lesions from 4 Tesla ex vivo

magnetic resonance imagery of the prostate [260]. Biochemical attempts to detect

prostate cancer have recently [261] included cytokine profiling of prostatic fluid from

cancerous prostate glands and correlation of cytokines with extent of tumor and

inflammation. Despite these technological and biochemical advances, therapeutic

options remain limited in patients with cancer that advances during hormone

manipulation and chemotherapy. Targeted therapies against receptor tyrosine kinases of

the ErbB family [262] have shown some promise in the treatment of HRPC; while

targeted inhibition of downstream pathways, namely mammalian target of rapamycin

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(mTOR) may prove to be important in the treatment of HRPC because of the prevalence

of phosphatase and tensin homolog deleted on chromosome 10 (PTEN) loss, in the light

of clinical evidence that mTOR inhibition reverses the phenotype of PTEN loss [262].

Cellular proliferation, differentiation, and environmental interactions each requires the

production, assembly, operation, and regulation of many thousands of components, and

they do so with remarkable fidelity in the face of many environmental cues and

challenges [263]. With the completion of the sequence for many organisms and the

potential for near-comprehensive catalogs of genes and regulatory regions for most

genes of the genome, new technologies have emerged that allow for global approaches

to dissecting signaling pathways and the more precise understanding of how signaling

components function within pathways [264]. Understanding how cellular and

developmental events occur at a molecular level with such precision has become a

major focus for modern molecular biology, and considerable effort has been devoted to

determining the regulatory networks that control and mediate complex biological

processes [263,264].

In the current study, we characterized and compared gene expression signatures in five

human prostate cancer cell lines (22Rv1, LNCaP, MDA PCa 2b, PC-3, and DU 145)

and a normal prostate epithelial cell line (PZ-HPV-7). Pathway prediction analyses

enabled us to generate functional biological networks for each cancerous cell line versus

the normal cell line, as well as networks for genes that were common to all cancerous

cell lines. Regulatory element analyses enabled the identification of Transcription

Factor Binding Sites (TFBS)-association signatures for the critical biomarkers identified

in prostate cancer. Overall, the differential expression of biological networks in prostate

219

cancer cell lines point to a perturbation of network homeostasis that may be mediated

by androgen-dependence or –independence in prostate cancer.


Cell culture : Five human prostate cancer cell lines (22Rv1, LNCaP, MDA PCa

2b, PC-3, and DU 145) and a normal prostate epithelial cell line (PZ-HPV-7) were

obtained from ATCC. The 22Rv1, LNCaP and MDA PCa 2b cells were cultured in

RPMI-1640 medium containing 10% Fetal Bovine Serum (FBS), whereas PC-3 and DU

145 cells were cultured in Minimum Essential Medium (MEM) containing 10% FBS.

The PZ-HPV-7 cells were cultured in Keratinocyte Serum Free Medium (K-SFM,

Gibco) containing 0.05mg/ml bovine pituitary extract and 5ng/ml epidermal growth

factor.

RNA extraction and Assessment of RNA Integrity : RNA was harvested

using the RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. RNA

integrity was assessed using formaldehyde gels in 1X MOPS buffer and RNA

concentration was determined by the 260/280 ratio on a DU 530 UV/Visible

spectrophotometer (Beckman).

Microarray Sample Preparation and Hybridization : Affymetrix (Affymetrix,

Santa Clara, CA) human genome U133 Plus 2.0 array was used to probe the global gene

expression profiles in RNA from the five cancerous and one non-cancerous prostate cell

lines. The cDNA synthesis and biotin-labeling of cRNA, hybridization and scanning of

the arrays, were performed at CINJ Core Expression Array Facility of Robert Wood

220

Johnson Medical School (New Brunswick, NJ). Each chip was hybridized with cRNA

derived from a single prostate cell line. Briefly, double-stranded cDNA was synthesized

from 5 µg of total RNA and labeled using the ENZO BioArray RNA transcript labeling

kit (Enzo Life Sciences, Inc.,Farmingdale, NY, USA) to generate biotinylated cRNA.

Biotin-labeled cRNA was purified and fragmented randomly according to Affymetrix’s

protocol. Two hundred microliters of sample cocktail containing 15 μg of fragmented

and biotin-labeled cRNA was loaded onto each chip. Chips were hybridized at 45°C for

16 h and washed with fluidics protocol EukGE-WS2v5 according to Affymetrix’s

recommendation. At the completion of the fluidics protocol, the chips were placed into

the Affymetrix GeneChip Scanner where the intensity of the fluorescence for each

feature was measured.

Affymetrix Data Analyses : The CEL files created from each sample (chip)

were first imported into dChip software [197,198] for further data characterization.

Briefly, a gene information file with current annotations and functional gene ontology

was generated and the Affymetrix Chip Description File (CDF) was specified. The data

were then normalized in dChip and the expression value for each gene was determined

by calculating the average of differences in intensity (perfect match intensity minus

mismatch intensity) between its probe pairs. Lists of genes that were modulated at least

five-fold in each cancerous prostate cell line as compared to non-cancerous prostate cell

line was generated. Thereafter, Clustering and Enrichment Analysis was performed to

obtain hierarchical tree clustering diagrams that provided functional classification of

Affymetrix Probe Set IDs and gene descriptions.

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Pathway Prediction Analyses : Functional analyses, biomarker filtration, and

metabolomics analyses were performed through the use of Ingenuity Pathways Analysis

(Ingenuity®

Systems, www.ingenuity.com). Starting with the list of genes that were

regulated at least five-fold in the dChip analyses described earlier, we performed

functional analyses by associating them with biological functions in the Ingenuity

Pathways Knowledge Base. Using Ingenuity’s Biomarker Filter, we identified

representative biomarkers that were greatly modulated (at least five-fold) in cancerous

prostate cell lines as compared to normal. The criteria used for the filter were disease

state (cancer), tissue type (specific prostate cell line as applicable) and species (human).

The candidate biomarkers were toggled to their subcellular localization. Further, we

subjected these candidate biomarkers to Ingenuity’s Metabolomics analysis and

identified biologically relevant signaling networks in prostate cancer. Briefly, a data set

containing gene identifiers (GenBank Accession IDs) and corresponding expression

values (obtained earlier from our dChip analyses) was uploaded into in the application.

Each gene identifier was mapped to its corresponding gene object in the Ingenuity

Pathways Knowledge Base. An expression value cut-off of 1.0 was set to identify all

genes whose expression was significantly differentially regulated. These genes, called

focus genes, were overlaid onto a global molecular network developed from

information contained in the Ingenuity Pathways Knowledge Base. Networks of these

focus genes were then algorithmically generated based on their connectivity.

Regulatory element analyses : The co-regulated potential biomarkers were

now additionally investigated for Transcription Factor Binding Site (TFBS)-association

222

signatures using NCBI’s DiRE (Distant Regulatory Elements of co-regulated genes) at

the DiRE server for the identification of distant regulatory elements of coregulated

genes (http://dire.dcode.org) and the top ten transcription factors were identified.

DiRE's unique feature is the detection of regulatory elements outside of proximal

promoter regions, as it takes advantage of the full gene locus to conduct the search.

Function-specific regulatory elements consisting of clusters of specifically-associated

TFBSs were predicted, and the association of individual transcription factors with the

biological function shared by the group of input genes were scored.

8.4. Results

8.4.1. Microarray analyses for cancerous and non-cancerous prostate cell lines

To investigate the global gene expression profiles in prostate cancer, we used androgen-

independent DU 145 and PC-3 cells, androgen-dependent 22Rv1 and MDA PCa 2b

cells, and androgen- and estrogen-dependent LNCaP cells as shown in Table 8.1. We

performed Affymetrix microarray analyses using Human Genome U133 Plus 2.0 array,

and analyzed the data using dChip application as discussed in Materials and Methods.

We compared them with normal prostate epithelial cell line PZ-HPV-7 and generated

lists of genes that were modulated at least five-fold in each of these cancerous cell lines

as compared to normal PZ-HPV-7 cells. Further, to restrict the study to genes that are

especially relevant in prostate cancer, we subjected these gene lists to biomarker

filtration and metabolomics analyses using pathway prediction that we will discuss

further. We also clustered the cell lines based on their global gene expression profiles as

shown in Figure 8.1. Moreover, we generated a list of genes that are common to all the

http://dire.dcode.org/

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five cancerous cell lines irrespective of their fold change values, the major members

being listed in Table 8.2. We used this list for further analyses as will be discussed later.

8.4.2. Biological Networks in different prostate cancer cell lines

To investigate the nature of the biological networks, and to detect perturbations in them

across different prostate cancer cell lines, we used the gene lists generated as discussed

above that were specific to each cancerous cell line versus the normal cell line, and

proceeded to perform pathway prediction analyses using Ingenuity Pathways Analysis

(Ingenuity®

Systems, www.ingenuity.com). Figure 8.2.A shows the complexity of the

network in androgen-dependent 22Rv1 cells with 261 members and multiple

connections that constitute the network. The major members in this network are seen in

the expanded Figure 8.2.B. Similarly, Figures 8.3.A and 8.4.A show the 260 members

in the androgen- and estrogen-dependent LNCaP network and the 237 members in the

androgen-dependent MDA PCa 2b network respectively. For clarity of presentation,

sub-network structures of the LNCaP and MDA PCa 2b networks are shown in Figures

8.3.B and 8.4.B respectively. In addition, Figure 8.5 depicts the major members of the

androgen-independent DU 145 network, whereas Figure 8.6 illustrates the major

members of the androgen-independent PC-3 network.

The major target hubs in 22Rv1 cells were STAT3, EGFR, TGF beta, CD44, Fos and

androgen receptor AR (Figure 8.2. B,C). In LNCaP cells, the major target hubs were

EGFR, Fos, AR, and TGF beta (Figure 8.3.B). The prominent target hubs in MDA PCa

2b cells were EGFR, CTNNB1 and AR (Figure 8.4.B). In DU 145 cells, the major

target hubs were IL6, EGFR, CD44 and CDKN1A (Figure 8.5), whereas, in PC-3 cells,

http://www.ingenuity.com/

224

the major target hubs were FOS, EGFR, FN1, FRAP1, CD44, CDH1 and SMARCA4.

The identification of these target hubs emphasizes their importance as critical regulatory

nodes that might govern events responsible for progression of prostate cancer at various

stages of carcinogenesis, rendering them as potential biomarkers for chemopreventive

or therapeutic intervention.

8.4.3. Metabolomics analyses

To investigate the different biological networks that operate in prostate cancer

independent of hormone status, we first subjected the list of genes that were common to

all cancerous cell lines, as shown in Table 8.2, to biomarker filtration and identified 57

candidate biomarkers using Ingenuity’s application as discussed in Materials and

Methods. Further, we subjected these 57 biomarkers to Ingenuity’s metabolomics

analyses and deciphered four regulatory networks from the crosstalk between these 57

candidate biomarkers based on known interactions accessed from the Ingenuity

Knowledge Base. These four networks included (i) Cell Death, Cancer, Cellular

Development (Figure 8.7), (ii) Cancer, Endocrine System Development and Function,

Organ Development (Figure 8.8), (iii) Cancer, Cell Death, Cellular Movement (Figure

8.9), and (iv) Gene Expression, Cell Cycle, Cancer (Figure 8.10). Further, to obtain a

comprehensive overview of regulation amongst all these networks, we merged them as

shown in Figure 8.11. A, with expanded versions being shown in Figures 8.11.B and

8.11.C for clarity of presentation.

Perusal of the metabolomics networks (Figures 8.7 through 8.11) enabled the

identification of a large number of critical signaling molecules as target hubs, including

225

AP-1, EGFR, p38 MAPK, TGF beta, NF-қB, Histone H3, VEGF, CD44, Akt, PI3K,

AR, CCND1, SMARCA4, CASP1, KLK3, MMP2, JNK, ERK1/2, PDGF, TP53, and

NOTCH1. The elucidation of feedback loops and cross-talk between these many

members reiterates the complexity of developing suitable agents for therapeutic or

preventive intervention strategies.

8.4.4. Regulatory element analyses

To investigate the TFBS-association signatures in the regulatory regions of the 57

candidate biomarkers we had identified earlier, we subjected them to DiRE analyses as

discussed in Materials and Methods. Interestingly, we found key transcription factor

signatures amongst the top ten hits that are depicted in Figure 8.12. Of note, LEF1,

NERF, HEB, ELK1 and MYOD were identified in the regulatory regions of these genes

that were expressed in prostate cancers.

8.5. Discussion

Prostate cancer displays considerable clinical, morphological, and biological

heterogeneity [265]. To understand the organization of the transcriptional networks that

govern the etiopathogenesis of prostate cancer, we investigated the transcriptional

circuitry controlling human prostate cancer in androgen-dependent, androgen- and

estrogen-dependent and androgen-independent prostate cancer cell lines. We present

graphical representations of global topology and local network motifs describing

network structures as well as sub-network structures for these conditional parameters

(hormone dependence) that might be important in carcinogenesis in the prostate gland.

226

The targets identified in the current study on differential regulation in prostate cancer

based on hormonal status are observed to form complex networks, manifesting distinct

patterns characteristic of autoregulation, feedback and feed-forward loops [266], and

cross-talk between themselves, thus, underscoring the complexity of cellular events that

govern progression from early lesion to clinical diagnosis of prostate cancer, or

metastatic potential of pre-existent high-grade PIN. Interestingly, the defining feature of

transient hubs is their capacity to change interactions between conditions [267]. In this

study, we observed key transient hubs such as CD44, EGFR and FOS that changed

interactions based on hormonal status of the cell line. This can have important

implications in progression of androgen-dependent prostate cancer to one that is

hormone refractory (HRPC), and, therefore, less amenable to successful outcomes in

clinical therapy.

Using a yeast developmental model, Borneman et al [266] reported excellent evidence

that target hubs can serve as master regulators whose activity is sufficient for the

induction of complex developmental responses, and, therefore, represent important

regulatory nodes in biological networks. Our results show that a large number of critical

signaling molecules revealed themselves as target hubs in the latent network structures,

including AP-1, EGFR, p38 MAPK, TGF beta, NF-қB, Histone H3, VEGF, CD44, Akt,

PI3K, AR, CCND1, SMARCA4, CASP1, KLK3, MMP2, JNK, ERK1/2, PDGF, TP53,

and NOTCH1. The biological role(s) of activator protein 1 (AP-1), epidermal growth

factor receptor (EGFR), nuclear factor kappa B (NF-қB) and mitogen-activated protein

kinases (ERK1/2, JNK, p38 MAPK) have been the subject of previous studies

[18,19,41,184,204,268] from our laboratory and several others [269-272] that elucidate

227

important roles for these proteins in prostate cancer. CD44 is known to be important for

prostate branching morphogenesis and metastasis to the bone microenvironment [273]

that has also been previously identified as a prognostic biomarker for biochemical

recurrence [274] among prostate cancer patients with clinically localized disease.

Vascular endothelial growth factor (VEGF) produced by tumor cells plays a central role

in stimulating angiogenesis required for solid tumor growth with a humanized

monoclonal anti-VEGF antibody (bevacizumab, i.e., Avastin) approved as a treatment

for metastatic cancer [275]. Human bone marrow activates the Akt pathway in

metastatic prostate cells through transactivation of the alpha-platelet-derived growth

factor (PDGF) receptor [276]. In addition, p53-mediated upregulation of Notch1

expression [277] in human prostate cancer cell lines is known to contribute to cell fate

determination after genotoxic stress. Thus, several master regulators of prostate cancer

were successfully identified in the current study.

We have shown [184] previously that ERK and JNK signaling pathways are involved in

the regulation of activator protein 1 and cell death elicited by chemopreventive

isothiocyanates in human prostate cancer. We have also reported [80] the suppression of

NF-κB by sulforaphane and phenethyl isothiocyanate in human prostate cancer. From

our current results on the metabolomics networks (Figure 8.11. A, B, C), we see that

major MAP kinases including ERK1/2, JNK and p38, as well as AP-1 and NF-κB

transcription factors are major target hubs, thus validating the relevance of our results

from a biological perpective. Further, we have observed [19] inhibition of EGFR

signaling in human prostate cancer PC-3 cells by combination treatment with

phenylethyl isothiocyanate and curcumin. Our results validated these observations with

228

EGFR appearing as a major target hub across all prostate cancer cell lines in this study

(Figures 8.2 through 8.6) as well as the merged comprehensive network (Figure 8.11

and its substructures).

The conceptual connections associated with progression confirm that prostate cancer

biology is largely driven by pathways related to androgen signaling and epithelial cell

biology; however, further analysis of concepts associated with progression suggests

stromal factors are highly associated with progression of prostate cancer [278]. Tomlins

[279] noted that although protein biosynthesis, E26 transformation-specific (ETS)

family transcriptional targets, androgen signaling and cell proliferation demarcate

critical transitions in progression, it was specifically high-grade cancer (Gleason pattern

4) that showed an attenuated androgen signaling signature, similar to metastatic prostate

cancer, which may reflect dedifferentiation and explain the clinical association of grade

with prognosis. In agreement with this, our results (Table 8.2) show attenuated

androgen receptor in both DU 145 and PC-3 androgen-independent cells that may be

indicative of a heightened possibility of progression to HRPC.

Cetuximab (Erbitux), Pertuzumab (Omnitarg) and Trastuzumab (Herceptin) are anti-

cancer drugs targeted to EGF family ligands, while Gefitinib (Iressa), Erlotinib

(Tarceva) and Lapatinib (GW572016) are anti-cancer drugs targeted to ERBB family

receptors [280]. TCF/LEF binding sites which exist within the promoter region of

human EGF family members were the topmost hits in our regulatory element analyses

for TFBS-association signatures (Figure 8.12) which reiterates the importance of the

valid network motifs that were identified in this study. Importantly, transcription factor

SOX9 which regulates androgen receptor expression in prostate cancer cells [281] was

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elicited in our regulatory study (Figure 8.12). Malignant transformation with acquired

androgen-independence of human prostate epithelial cells has been shown [282] to be

correlated with increased expression of ELK1 and MEK1/2, thus the identification of

ELK1 in Figure 8.12 is significant. Thus, important prognostic biomarkers for

development of novel chemopreventive and therapeutic strategies in prostate cancer

were potentially identified from our TFBS-association studies.

Rhodes and Chinnaiyan [89] noted that although microarray profiling can to some

extent decipher the molecular heterogeneity of cancer, integrative analyses that evaluate

cancer transcriptome data in the context of other data sources are more capable of

extracting deeper biological insight from the data. Indeed, our integrative computational

and analytical approaches, including TFBS-association signature analyses and

transcriptional network analyses are a step in this direction with the ultimate aim of

enhancing our understanding of prognostic biomarkers and key signaling pathways that

are important in prostate cancer progression, thus delineating functionally relevant

targets for chemopreventive or therapeutic intervention. Taken together, the elucidation

in this study of several target hubs in the latent structure of biological networks

generated in hormone-dependent and hormone-independent cell lines, and the

delineation of key TFBS-association signatures, would enhance our understanding of

regulatory nodes that might be central to identifying key players in cellular signal

transduction cascades that are important in the pathogenesis of prostate cancer.

230


Sujit Nair is extremely thankful to Dr. Li Cai for his expertise and patient training on

the bioinformatic approaches used in this study. This work was supported in part by

RO1 CA 118947 to Ah-Ng Tony Kong from the National Institutes of Health (NIH).

231

Figure 8.1. Clustering of human prostate cell lines used in the study

Human prostate cell lines used in this study were clustered on the basis of global gene

expression using dChip analyses.

232

Figure 8.2. A.

233

Figure 8.2. B.

234

Figure 8.2. C.

235

Figure 8.2. Biological network in androgen-dependent human prostate cancer

22Rv1 cells

8.2.A. 261 network motifs comprising the biological network for genes that are

modulated at least five-fold in 22Rv1 cells as compared to normal prostate epithelial

PZ-HPV-7 cells ; 8.2.B. Sub-network structure in 22Rv1 network, upper panel ; 8.2.C.

Sub-network structure in 22Rv1 network, lower panel.

236

Figure 8.3. A.

237

Figure 8.3. B.

238

Figure 8.3. Biological network in androgen- and estrogen-dependent human

prostate cancer LNCaP cells


modulated at least five-fold in LNCaP cells as compared to normal prostate epithelial

PZ-HPV-7 cells ; 8.3.B. Sub-network structure in LNCaP network.

239

Figure 8.4. A.

240

Figure 8.4. B.

241

Figure 8.4. Biological network in androgen-dependent human prostate cancer

MDA PCa 2b cells


modulated at least five-fold in MDA PCa 2b cells as compared to normal prostate

epithelial PZ-HPV-7 cells ; 8.4.B. Sub-network structure in MDA PCa 2b network.

242

Figure 8.5. Biological network in androgen-independent human prostate cancer

DU 145 cells

Major motifs comprising the DU 145 network for genes that are modulated at least five-

fold in DU 145 cells as compared to normal prostate epithelial PZ-HPV-7 cells are

shown.

243

Figure 8.6. Biological network in androgen-independent human prostate cancer

PC-3 cells

Major motifs comprising the PC-3 network for genes that are modulated at least five-

fold in PC-3 cells as compared to normal prostate epithelial PZ-HPV-7 cells are shown.

244

Figure 8.7. Cell Death, Cancer, Cellular Development Network

Network motifs for the Cell Death, Cancer, Cellular Development Network elicited by

metabolomics analyses of genes modulated in all prostate cancer cell lines used in this

study.

245

Figure 8.8. Cancer, Endocrine System Development and Function, Organ

Development Network

Network motifs for the Cancer, Endocrine System Development and Function, Organ

Development Network elicited by metabolomics analyses of genes modulated in all

prostate cancer cell lines used in this study.

246

Figure 8.9. Cancer, Cell Death, Cellular Movement Network

Network motifs for the Cancer, Cell Death, Cellular Movement Network elicited by


study.

247

Figure 8.10. Gene Expression, Cell Cycle, Cancer Network

Network motifs for the Gene Expression, Cell Cycle, Cancer Network elicited by


study.

248

Figure 8.11. A.

249

Figure 8.11. B.

250

Figure 8.11. C.

251

Figure 8.11. Comprehensive overview of all Metabolomics Networks in prostate

cancer cell lines

8.11.A. All biological networks elicited by metabolomics analyses (Figures 8.7 through

8.10) were merged to obtain a comprehensive overview of all associations, feedback

and feed-forward loops, cross-talk and major target hubs that may be important in

prostate cancer. 8.11.B. Sub-network structure in comprehensive metabolomics

network, upper panel ; 8.11.C. Sub-network structure in comprehensive metabolomics

network, lower panel.

252

Figure 8.12. Regulatory element analyses for identification of TFBS-association

signatures

TFBS-association signatures in the regulatory regions of the candidate biomarkers were

identified by DiRE analyses. The top ten transcription factors based on occurrence and

importance are shown here.

253

APPENDIX

254

Table 1.1. Major Phase II detoxifying/antioxidant genes upregulated by select

dietary chemopreventive agents and downregulated by a toxicant via the redox-

sensitive transcription factor Nrf2.

BHA, Butylated Hydroxyanisole; EGCG, epigallocatechin-3-gallate; CUR,

Curcumin; SFN, Sulforaphane; PEITC, Phenethyl isothiocyanate; TM,

Tunicamycin (a toxicant); Y denotes Yes and N denotes No (genes modulated or

not modulated respectively by the chemopreventive agents/toxicant).

255

Table 2.1.Oligonucleotide primers used in quantitative real-time PCR (qRT-PCR)

256

Gene Description Symbol GenBank SIT LiverAccession No a b

Cell Adhesionactivated leukocyte cell adhesion molecule Alcam NM_009655 2.39CD47 antigen (Rh-related antigen, integrin-associated signal transducer) Cd47 NM_010581 2.02Apoptosis and cell cycle controlanaphase promoting complex subunit 1 Anapc1 NM_008569 28.6BCL2-like 11 (apoptosis facilitator) Bcl2l11 NM_207680 2.1CASP8 and FADD-like apoptosis regulator Cflar NM_207653 3.94caspase recruitment domain family, member 6 Card6 XM_139295 2.44cyclin G1 Ccng1 NM_009831 2.61cyclin T2 Ccnt2 NM_028399 2.37G1 to S phase transition 1 Gspt1 NM_146066 2.9growth arrest and DNA-damage-inducible 45 alpha Gadd45a NM_007836 2.65growth arrest and DNA-damage-inducible 45 alpha Gadd45a NM_007836 3.02growth arrest and DNA-damage-inducible 45 beta Gadd45b NM_008655 3.19growth arrest specific 1 Gas1 NM_008086 2.17growth arrest-specific 2 like 3 Gas2l3 XM_137276 2.23insulin-like growth factor binding protein 3 Igfbp3 NM_008343 2.26Nuclear mitotic apparatus protein 1 (Numa1), mRNA Numa1 NM_133947 2retinoblastoma binding protein 8 Rbbp8 XM_484703 2.38synaptonemal complex protein 1 Sycp1 NM_011516 2.21Tnf receptor-associated factor 1 Traf1 NM_009421 5.57Proliferating cell nuclear antigen Pcna NM_011045 4.76 9.76Biosynthesis and Metabolismaldehyde dehydrogenase family 1, subfamily A3 Aldh1a3 NM_053080 2.99acyl-CoA synthetase long-chain family member 1 Acsl1 NM_007981 2.05acyl-Coenzyme A binding domain containing 3 Acbd3 NM_133225 2.01Adenylate kinase 3 (Ak3), mRNA Ak3l1 NM_021299 2.38aldehyde dehydrogenase 2, mitochondrial Aldh2 NM_009656 2.93arginine decarboxylase Adc NM_172875 2.86creatine kinase, mitochondrial 2 Ckmt2 NM_198415 10.07creatine kinase, muscle Ckm NM_007710 5.17creatine kinase, muscle Ckm NM_007710 3.88dopamine beta hydroxylase Dbh NM_138942 2.45ectonucleoside triphosphate diphosphohydrolase 3 Entpd3 NM_178676 5.54eukaryotic translation initiation factor 3, subunit 10 (theta) Eif3s10 NM_010123 2.02galactose-4-epimerase, UDP Gale NM_178389 2Glyoxalase 1 Glo1 NM_025374 2.05GTP binding protein 2 Gtpbp2 NM_019581 2.08GTP binding protein 2 Gtpbp2 NM_019581 2.68guanine nucleotide binding protein-like 2 (nucleolar) Gnl2 NM_145552 2.25guanine nucleotide binding protein-like 2 (nucleolar) Gnl2 NM_145552 2.16guanosine monophosphate reductase Gmpr NM_145465 3.79hydroxysteroid (17-beta) dehydrogenase 7 Hsd17b7 NM_010476 2.19nitric oxide synthase 3 antisense Nos3as NM_0010028 3.14phosphatidylinositol glycan, classC Pigc NM_026078 7.44phosphoglucomutase 2-like 1 Pgm2l1 NM_027629 14.39phospholipase A2, group IIF Pla2g2f NM_012045 2.06prostaglandin-endoperoxide synthase 2 Ptgs2 NM_011198 4.9

Table 2.2. BHA-induced Nrf2-dependent genes in mouse small intestine and

liver…..continued…

257


stearoyl-Coenzyme A desaturase 1 Scd1 NM_009127 3.39tryptophan hydroxylase 1 Tph1 NM_009414 8.64very low density lipoprotein receptor Vldlr NM_013703 2.24Cell Growth and Differentiationcysteine rich transmembrane BMP regulator 1 (chordin like) Crim1 NM_015800 3.44FK506 binding protein 12-rapamycin associated protein 1 Frap1 NM_020009 5.35motile sperm domain containing 3 Mospd3 NM_030037 2.21neurotrophin 5 Ntf5 NM_198190 5.42tropomodulin 1 Tmod1 NM_021883 2.25tissue inhibitor of metalloproteinase 2 Timp2 NM_011594 3.02Detoxification enzymescarbohydrate sulfotransferase 10 Chst10 NM_145142 2.44esterase D/formylglutathione hydrolase Esd NM_016903 4.65Esterase D/formylglutathione hydrolase (Esd), mRNA Esd NM_016903 2.53fucosyltransferase 4 Fut4 NM_010242 2.02glutamate-cysteine ligase, catalytic subunit Gclc NM_010295 2.69glutamate-cysteine ligase, catalytic subunit Gclc NM_010295 2.14glutathione S-transferase, mu 1 Gstm1 NM_010358 3.67glutathione S-transferase, mu 3 Gstm3 NM_010359 3.39heme oxygenase (decycling) 1 Hmox1 NM_010442 3.12heparan sulfate 6-O-sulfotransferase 2 Hs6st2 NM_015819 6.54ST3 beta-galactoside alpha-2,3-sialyltransferase 2 St3gal2 NM_009179 2.9thioesterase superfamily member 5 Them5 NM_025416 5.53UDP glucuronosylltransferase 8A Ugt8a NM_011674 5.65UDP glucuronosyltransferase 2 family, polypeptide B35 Ugt2b35 NM_172881 2.3Uronyl-2-sulfotransferase Ust NM_177387 2.32DNA Replicationtopoisomerase (DNA) I Top1 NM_009408 2.08origin recognition complex, subunit 2-like (S. cerevisiae) Orc2l NM_008765 2.15Electron Transportflavin containing monooxygenase 2 Fmo2 NM_018881 8.72monoamine oxidase B Maob NM_172778 2.16thioredoxin domain containing 10 Txndc10 NM_198295 3.85G-protein coupled receptorsangiotensin receptor 1 Agtr1 NM_177322 4.29Calmodulin III (Calm3) mRNA, 3' untranslated region Calm3 NM_007590 5.59chemokine (C-X-C motif) receptor 4 Cxcr4 NM_009911 2.3coagulation factor II (thrombin) receptor-like 2 F2rl2 NM_010170 2.05G protein-coupled receptor 133 Gpr133 XM_485685 16.59G protein-coupled receptor 20 Gpr20 NM_173365 2.1guanine nucleotide binding protein (G protein), gamma 3 subunit Gng3 NM_010316 2.64guanine nucleotide binding protein (G protein), gamma 7 subunit Gng7 NM_010319 2.1guanine nucleotide binding protein, alpha inhibiting 1 Gnai1 NM_010305 2.11regulator of G-protein signaling 9 Rgs9 NM_011268 7.73regulator of G-protein signaling 9 Rgs9 AK085443 13.73Kinases and Phosphatasesprotein tyrosine phosphatase, receptor type, T Ptprt NM_021464 2.27Serine/threonine kinase 24 (STE20 homolog, yeast) Stk24 NM_145465 3.84diacylglycerol kinase kappa Dagkk NM_177914 2.26

Table 2.2…..continued…

258


ethanolamine kinase 1 Etnk1 XM_284250 3.46FMS-like tyrosine kinase 4 Flt4 NM_008029 2.95inhibitor of kappaB kinase gamma Ikbkg NM_010547 3.92Janus kinase 2 Jak2 NM_008413 3.09microtubule associated serine/threonine kinase-like Mastl NM_025979 14.5mitogen activated protein kinase 8 Mapk8 NM_016700 10.39mitogen-activated protein kinase 6 Mapk6 NM_015806 2.11mitogen-activated protein kinase kinase kinase 9 Map3k9 NM_177395 3.06mitogen-activated protein kinase kinase kinase kinase 4 Map4k4 NM_008696 2.46mitogen-activated protein kinase kinase kinase kinase 5 Map4k5 NM_201519 3.94neurotrophic tyrosine kinase, receptor, type 1 Ntrk1 XM_283871 3.88neurotrophic tyrosine kinase, receptor, type 2 Ntrk2 NM_0010250 3.3protein kinase C, epsilon Prkce NM_011104 5.69protein kinase C, eta Prkch NM_008856 2.87protein kinase, cAMP dependent regulatory, type I, alpha Prkar1a AK049832 11.6protein kinase, cAMP dependent regulatory, type II alpha Prkar2a NM_008924 2.18protein phosphatase 1 (formerly 2C)-like Ppm1l NM_178726 2.48protein phosphatase 1, regulatory (inhibitor) subunit 14A Ppp1r14a NM_026731 2.1protein phosphatase 2, regulatory subunit B (B56), epsilon isoform Ppp2r5e NM_012024 2.12protein phosphatase 3, catalytic subunit, beta isoform Ppp3cb NM_008914 2.02protein tyrosine phosphatase 4a1 Ptp4a1 NM_011200 3.79protein tyrosine phosphatase, receptor type Z, polypeptide 1 Ptprz1 XM_620293 2.34Putative membrane-associated guanylate kinase 1 (Magi-1), Baiap1 NM_010367 2.45 alternatively spliced c formTANK-binding kinase 1 Tbk1 NM_019786 2.06Ttk protein kinase Ttk NM_009445 2.23tyrosine kinase, non-receptor, 1 Tnk1 NM_031880 2.34RNA/Protein processing and Nuclear assemblyHeterogeneous nuclear ribonucleoprotein A1 Hnrpa1 NM_010447 3.45histone 1, H4f Hist1h4f NM_175655… 7.1hypoxia up-regulated 1 Hyou1 NM_021395 3.02Methionine sulfoxide reductase A Msra NM_026322 2.9methionine sulfoxide reductase B3 Msrb3 NM_177092 3.41mitochondrial ribosomal protein L52 Mrpl52 NM_026851 5.18neuronal pentraxin receptor Nptxr NM_030689 3.29nucleosome assembly protein 1-like 1 Nap1l1 NM_015781 2.21peptidyl arginine deiminase, type II Padi2 NM_008812 3.28RNA polymerase 1-2 Rpo1-2 NM_009086 2.99RNA polymerase 1-4 Rpo1-4 NM_009088 35.71sarcolemma associated protein Slmap NM_032008 2.17tubulin tyrosine ligase-like family, member 4 Ttll4 NM_001014974 2.22importin 7 Ipo7 NM_181517 2.51karyopherin (importin) alpha 1 Kpna1 NM_008465 29.9Transcription factors and interacting partnersheat shock protein, alpha-crystallin-related, B6 Hspb6 NM_0010124 3.72bone morphogenetic protein receptor, type 1A Bmpr1a NM_009758 2.02metallothionein 1 Mt1 NM_013602 6.24insulin-like growth factor 2 Igf2 NM_010514 2.59Activating signal cointegrator 1 complex subunit 2 Ascc2 NM_029291 3.32


259


calsequestrin 1 Casq1 NM_009813 2CBFA2T1 identified gene homolog (human) Cbfa2t1h NM_009822 2.42CCCTC-binding factor Ctcf NM_007794 2.35C-erbA alpha1 mRNA for thyroid hormone receptor Thra NM_178060 2.74checkpoint supressor 1 Ches1 NM_183186 2.26circadian locomoter output cycles kaput Clock NM_007715 2.81Eph receptor A3 Epha3 NM_010140 2.23Eph receptor B1 Ephb1 NM_173447 2.34fos-like antigen 2 Fosl2 NM_008037 2.36Hedgehog-interacting protein Hhip NM_020259 6.11hepatoma-derived growth factor, related protein 2 Hdgfrp2 NM_008233 2.22hypermethylated in cancer 2 Hic2 NM_178922 2.82Insulin-like growth factor 2 receptor Igf2r NM_010515 2Jun oncogene Jun NM_010591 2.75Kruppel-like factor 7 (ubiquitous) Klf7 NM_033563 2.21lymphoblastomic leukemia Lyl1 NM_008535 7.36MAX dimerization protein 1 Mxd1 NM_010751 2.05NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 2 Ndufb2 NM_026612 3.43Notch gene homolog 4 (Drosophila) Notch4 NM_010929 2.35nuclear factor, interleukin 3, regulated Nfil3 NM_017373 17.13nuclear receptor co-repressor 1 Ncor1 NM_011308 3.39nuclear receptor interacting protein 1 Nrip1 NM_173440 2.61 2.63nuclear receptor subfamily 2, group C, member 2 Nr2c2 NM_011630 2.32nuclear receptor subfamily 6, group A, member 1 Nr6a1 NM_010264 2.49Phosphodiesterase 8A Pde8a NM_008803 2.58phosphoprotein associated with glycosphingolipid microdomains 1 Pag1 NM_053182 2.23Polycystic kidney disease 1 like 3 Pkd1l3 NM_181544 8.44polymerase (RNA) I associated factor 1 Praf1 NM_022811 2.28Pre B-cell leukemia transcription factor 3 Pbx3 NM_016768 2.05Purine rich element binding protein B Purb NM_011221 2.04RAB4A, member RAS oncogene family Rab4a BG080696 16.85Ras and Rab interactor 1 Rin1 NM_145495 3.11Ras association (RalGDS/AF-6) domain family 2 Rassf2 NM_175445 8.21ras homolog gene family, member T1 Rhot1 NM_021536 2.28reticuloendotheliosis oncogene Rel NM_009044 2.13retinol binding protein 1, cellular Rbp1 NM_011254 2.33serum response factor binding protein 1 Srfbp1 NM_026040 4.47Spred-1 Spred1 NM_033524 43.87suppressor of cytokine signaling 5 Socs5 NM_019654 19.3T-box brain gene 1 Tbr1 NM_009322 28.35thyroid hormone receptor beta Thrb NM_009380 2.43transcription factor AP-2 beta Tcfap2b NM_009334 3.79transducer of ERBB2, 2 Tob2 NM_020507 2.23transforming growth factor beta 1 induced transcript 1 Tgfb1i1 NM_009365 3.36transforming growth factor, beta receptor I Tgfbr1 NM_009370 14.27tumor necrosis factor receptor superfamily, member 19 Tnfrsf19 NM_013869 3.49tumor necrosis factor receptor superfamily, member 23 Tnfrsf23 NM_024290 2.6tumor necrosis factor, alpha-induced protein 2 Tnfaip2 NM_009396 2.13V-abl Abelson murine leukemia viral oncogene 2 Abl2 NM_009595 7.44


260


v-maf musculoaponeurotic fibrosarcoma oncogene family, protein G (avian) MafG NM_010756 2.45wingless-type MMTV integration site 9B Wnt9b NM_011719 3.35Yamaguchi sarcoma viral (v-yes) oncogene homolog 1 Yes1 NM_009535 4.21Zinc finger homeobox 1b (Zfhx1b), mRNA Zfhx1b NM_015753 2.6zinc finger protein 37 Zfp37 NM_009554 3.42RAN, member RAS oncogene family Ran NM_009391 2Transportaquaporin 1 Aqp1 NM_007472 2.37ATP-binding cassette, sub-family B (MDR/TAP), member 1A Abcb1a NM_011076 2.08ATP-binding cassette, sub-family C (CFTR/MRP), member 1 Abcc1 NM_008576 2.23ATP-binding cassette, sub-family D (ALD), member 3 Abcd3 NM_008991 2.21basic leucine zipper nuclear factor 1 Blzf1 NM_025505 6.45chloride channel calcium activated 4 Clca4 NM_139148 2.64cholinergic receptor, nicotinic, alpha polypeptide 3 Chrna3 NM_145129 2.01glutamate receptor, ionotropic, AMPA4 (alpha 4) Gria4 NM_019691 6.76Multidrug resistance-associated protein 3 (Abcc3) Abcc3 NM_029600 2.44potassium large conductance calcium-activated channel, subfamily M, beta member 2 Kcnmb2 NM_028231 3.63Proton/amino acid transporter 4 (PAT4) Slc36a4 NM_172289 3.04sideroflexin 2 Sfxn2 NM_053196 2.05signal transducing adaptor molecule (SH3 domain and ITAM motif) 2 Stam2 NM_019667 2.21sodium channel, voltage-gated, type VII, alpha Scn7a NM_009135 2.19solute carrier family 16 (monocarboxylic acid transporters), member 10 Slc16a10 NM_028247 2.38solute carrier family 35, member A5 Slc35a5 NM_028756 3.32solute carrier family 39 (zinc transporter), member 10 Slc39a10 NM_172653 8.55solute carrier family 5 (sodium/glucose cotransporter), member 12 Slc5a12 NM_0010039 3.69solute carrier organic anion transporter family, member 2a1 Slco2a1 NM_033314 2.3Type III sodium-dependent phosphate transporter (Slc20a2) Slc20a2 NM_011394 2.71Ubiquitination and Proteolysisa disintegrin and metallopeptidase domain 10 Adam10 NM_007399 3.45cathepsin B Ctsb NM_007798 2.06Constitutive photomorphogenic protein (Cop1) Rfwd2 NM_011931 2.05HECT domain containing 2 Hectd2 NM_172637 2serine carboxypeptidase 1 Scpep1 NM_029023 2.05SUMO/sentrin specific peptidase 2 Senp2 NM_029457 4.15SUMO1/sentrin specific peptidase 1 Senp1 NM_144851 2.62ubiquitin-activating enzyme E1, Chr Y 1 Ube1y1 NM_011667 2.39OthersU1 small nuclear ribonucleoprotein 1C Snrp1c NM_011432 3.21melanoma antigen, family H, 1 Mageh1 NM_023788 5.84HIV TAT specific factor 1 Htatsf1 NM_028242 2.1chemokine (C-X-C motif) ligand 2 Cxcl2 NM_009140 2.1DEAD (Asp-Glu-Ala-Asp) box polypeptide 10 Ddx10 XM_284494 3.42DEAD (Asp-Glu-Ala-Asp) box polypeptide 5 Ddx5 NM_007840 3.05golgi phosphoprotein 4 Golph4 NM_175193 2.37kelch-like 7 (Drosophila) Klhl7 NM_026448 2.95leucine rich repeat containing 48 Lrrc48 NM_029044 9.96leucine-rich repeats and immunoglobulin-like domains 3 Lrig3 NM_177152 8.43protein disulfide isomerase associated 3 Pdia3 NM_007952 2serine (or cysteine) peptidase inhibitor, clade I, member 1 Serpini1 NM_009250 2.47


261


six transmembrane epithelial antigen of prostate 2 Steap2 XM_284053 2.96TCDD-inducible poly(ADP-ribose) polymerase Tiparp NM_178892 2.5tyrosine 3-monooxygenase/tryptophan 5-monooxygenase, Ywhaz NM_011740 2.17 3.46activation protein, zeta polypeptidekeratin associated protein 6-1 Krtap6-1 NM_010672 5.04keratin complex 2, basic, gene 18 Krt2-18 NM_016879 5.85seminal vesicle secretion 3 Svs3 NM_021363 8.7antigen p97 (melanoma associated) Mfi2 NM_013900 6.73

aGenes that were induced >2-fold by BHA only in small intestine of Nrf2 wild-type

mice but not in small intestine of Nrf2 knockout mice compared with vehicle treatment

at 3h. The relative mRNA expression levels of each gene in treatment group over

vehicle group (fold changes) are listed.

bGenes that were induced >2-fold by BHA only in liver of Nrf2 wild-type mice but not

in liver of Nrf2 knockout mice compared with vehicle treatment at 3h. The relative

mRNA expression levels of each gene in treatment group over vehicle group (fold

changes) are listed.

Table 2.2.

262

Gene Description GenBank Symbol SIT LiverAccession No. a b

Cell Adhesioncamello-like 2 NM_053096 Cml2 0.03catenin (cadherin associated protein), delta 2 NM_008729 Ctnnd2 0.36intercellular adhesion molecule NM_010493 Icam1 0.48protocadherin 7 NM_018764 Pcdh7 0.43Apoptosis and cell cycle controlB-cell leukemia/lymphoma 2 NM_009741 Bcl2 0.32breast cancer 1 NM_009764 Brca1 0.49CASP2 and RIPK1 domain containing adaptor with death domain NM_009950 Cradd 0.39Cell division cycle 37 homolog (S. cerevisiae)-like 1 NM_025950 Cdc37l1 0.28cell division cycle and apoptosis regulator 1 NM_026201 Ccar1 0.4centrin 4 NM_145825 Cetn4 0.21Cyclin I NM_017367 Ccni 0.28G0/G1 switch gene 2 NM_008059 G0s2 0.44integrin beta 1 (fibronectin receptor beta) NM_010578 Itgb1 0.18nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1 NM_016791 Nfatc1 0.09Rho GTPase activating protein 4 NM_138630 Arhgap4 0.38WW domain-containing oxidoreductase NM_019573 Wwox 0.47Biosynthesis and Metabolismabhydrolase domain containing 9 XM_128553 Abhd9 0.22Acetyl-Coenzyme A carboxylase beta NM_133904 Acacb 0.39alanine-glyoxylate aminotransferase NM_016702 Agxt 0.48B-cell CLL/lymphoma 9-like XM_620743 Bcl9l 0.1butyrobetaine (gamma), 2-oxoglutarate dioxygenase 1 NM_130452 Bbox1 0.42(gamma-butyrobetaine hydroxylase)methylmalonyl-Coenzyme A mutase NM_008650 Mut 0.17mitochondrial ribosomal protein L27 NM_053161 Mrpl27 0.47mitochondrial ribosomal protein S14 NM_025474 Mrps14 0.35mitochondrial ribosomal protein S21 NM_078479 Mrps21 0.49nicotinamide nucleotide adenylyltransferase 2 NM_175460 Nmnat2 0.33pantothenate kinase 1 NM_023792 Pank1 0.18phosphoglucomutase 2-like 1 NM_027629 Pgm2l1 0.4phosphopantothenoylcysteine decarboxylase NM_176831 Ppcdc 0.05phosphoribosyl pyrophosphate synthetase 2 NM_026662 Prps2 0.47propionyl-Coenzyme A carboxylase, alpha polypeptide NM_144844 Pcca 0.47UDP-glucose ceramide glucosyltransferase NM_011673 Ugcg 0.37ureidopropionase, beta NM_133995 Upb1 0.45uroporphyrinogen III synthase NM_009479 Uros 0.36very low density lipoprotein receptor NM_013703 Vldlr 0.43Nitric oxide synthase 1, neuronal NM_008712 Nos1 0.4Cell Growth and Differentiationhelicase, lymphoid specific NM_008234 Hells 0.33male enhanced antigen 1 NM_010787 Mea1 0.48myosin, light polypeptide 7, regulatory NM_022879 Myl7 0.46DNA Replicationorigin recognition complex, subunit 4-like (S. cerevisiae) NM_011958 Orc4l 0.1Electron Transportcytochrome c oxidase, subunit VIIa 1 NM_009944 Cox7a1 0.42 0.4

Table 2.3. BHA-suppressed Nrf2-dependent genes in mouse small intestine and


263


cytochrome P450, family 21, subfamily a, polypeptide 1 NM_009995 Cyp21a1 0.29cytochrome P450, family 3, subfamily a, polypeptide 44 NM_177380 Cyp3a44 0.38cytochrome P450, family 7, subfamily a, polypeptide 1 NM_007824 Cyp7a1 0.37dual oxidase 1 XM_130483 Duox1 0.39NADH dehydrogenase (ubiquinone) 1 beta subcomplex 4 NM_026610 Ndufb4 0.41thioredoxin reductase 2 NM_013711 Txnrd2 0.21ubiquinol-cytochrome c reductase (6.4kD) subunit NM_025650 Uqcr 0.4G-protein coupled receptorsadrenergic receptor, beta 1 NM_007419 Adrb1 0.45bombesin-like receptor 3 NM_009766 Brs3 0.35calmodulin 3 NM_007590 Calm3 0.45cholecystokinin A receptor NM_009827 Cckar 0.19G protein-coupled receptor 1 NM_146250 Gpr1 0.45G protein-coupled receptor 37 NM_010338 Gpr37 0.1GNAS (guanine nucleotide binding protein, alpha stimulating) complex locus NM_010309 Gnas 0.46regulator of G-protein signaling 2 NM_009061 Rgs2 0.37vomeronasal 1 receptor, B1 NM_053225 V1rb1 0.5Kinases and Phosphatasescalcium/calmodulin-dependent protein kinase II, delta NM_001025439 Camk2d 0.38G protein-coupled receptor kinase 5 (GRK5) NM_018869 Gprk5 0.46glycogen synthase kinase 3 beta NM_019827 Gsk3b 0.15inositol polyphosphate multikinase NM_027184 Ipmk 0.47inositol polyphosphate-1-phosphatase NM_008384 Inpp1 0.37MAP kinase-activated protein kinase 5 NM_010765 Mapkapk5 0.42microtubule associated serine/threonine kinase family member 4 XM_283179 Mast4 0.12microtubule associated serine/threonine kinase family member 4 XM_283179 Mast4 0.48mitogen activated protein kinase kinase 7 NM_011944 Map2k7 0.37Mitogen-activated protein kinase associated protein 1 NM_177345 Mapkap1 0.34multiple substrate lipid kinase NM_023538 Mulk 0.48p21 (CDKN1A)-activated kinase 3 NM_008778 Pak3 0.42phosphoinositide-3-kinase, regulatory subunit 5, p101 NM_177320 Pik3r5 0.46Protein kinase ATR (Atr) NM_028533 Prk 0.49protein kinase, AMP-activated, alpha 1 catalytic subunit NM_001013367 Prkaa1 0.35Protein phosphatase 3, catalytic subunit, alpha isoform NM_008913 Ppp3ca 0.37Protein phosphatase 3, catalytic subunit, alpha isoform NM_008913 Ppp3ca 0.1Protein tyrosine phosphatase, non-receptor type 4 NM_019933 Ptpn4 0.5receptor tyrosine kinase-like orphan receptor 1 NM_013845 Ror1 0.29ribosomal protein S6 kinase, polypeptide 4 NM_019924 Rps6ka4 0.45Ribosomal protein S6 kinase, polypeptide 5 NM_153587 Rps6ka5 0.3RNA/Protein processing and Nuclear assemblyaspartate-beta-hydroxylase NM_023066 Asph 0.17ATP synthase mitochondrial F1 complex assembly factor 2 NM_145427 Atpaf2 0.48chromatin accessibility complex 1 NM_053068 Chrac1 0.49Down syndrome critical region gene 3 NM_007834 Dscr3 0.49fucosyltransferase 9 NM_010243 Fut9 0.05histone 3, H2a NM_178218 Hist3h2a 0.43neuronal pentraxin receptor NM_030689 Nptxr 0.37nucleoporin 133 NM_172288 Nup133 0.34protein-O-mannosyltransferase 2 NM_153415 Pomt2 0.46


264


Zinc finger, matrin-like NM_008717 Zfml 0.44Transcription factors and interacting partnersactivating transcription factor 7 interacting protein 2 XM_148109 Atf7ip2 0.35angiotensin II, type I receptor-associated protein NM_009642 Agtrap 0.49Bone morphogenetic protein 6 NM_007556 Bmp6 0.12Breast carcinoma amplified sequence 3 NM_138681 Bcas3 0.4E2F transcription factor 5 NM_007892 E2f5 0.38EGF-like module containing, mucin-like, hormone receptor-like sequence 4 NM_139138 Emr4 0.19epidermal growth factor receptor pathway substrate 15 NM_007943 Eps15 0.19Fc receptor, IgG, high affinity I NM_010186 Fcgr1 0.48FEV (ETS oncogene family) NM_153111 Fev 0.48Growth hormone receptor NM_010284 Ghr 0.31GTP binding protein 7 (putative) NM_199301 Gtpbp7 0.49heat shock factor 2 NM_008297 Hsf2 0.08HRAS-like suppressor NM_013751 Hrasls 0.23Human immunodeficiency virus type I enhancer binding protein 2 NM_010437 Hivep2 0.48Hypoxia inducible factor 1, alpha subunit NM_010431 Hif1a 0.4insulin-like growth factor 1 NM_010512 Igf1 0.43Insulin-like growth factor I receptor NM_010513 Igf1r 0.49interleukin 2 receptor, gamma chain NM_013563 Il2rg 0.44Kruppel-like factor 1 (erythroid) NM_010635 Klf1 0.43lysosomal trafficking regulator NM_010748 Lyst 0.43metal response element binding transcription factor 2 NM_013827 Mtf2 0.12myc target 1 NM_026793 Myct1 0.43Nuclear receptor coactivator 3 NM_013827 Ncoa3 0.12nuclear receptor subfamily 2, group F, member 1 NM_010151 Nr2f1 0.4peroxisome biogenesis factor 7 NM_008822 Pex7 0.5protein inhibitor of activated STAT 4 NM_021501 Pias4 0.45RAB28, member RAS oncogene family NM_027295 Rab28 0.46Rho GTPase activating protein 29 NM_172525 Arhgap29 0.26SH2 domain containing 4A XM_134197 Sh2d4a 0.23Sp2 transcription factor NM_030220 Sp2 0.45sphingosine kinase 2 NM_203280 Sphk2 0.45src family associated phosphoprotein 1 NM_001033186 Scap1 0.41Suppressor of cytokine signaling 2 NM_007706 Socs2 0.27Tax1 (human T-cell leukemia virus type I) binding protein 3 NM_029564 Tax1bp3 0.39thrombopoietin NM_009379 Thpo 0.19thyroid hormone receptor associated protein 1 XM_109726 Thrap1 0.37topoisomerase I binding, arginine/serine-rich NM_134097 Topors 0.49v-raf murine sarcoma 3611 viral oncogene homolog NM_009703 Araf 0.41Transportaquaporin 3 NM_016689 Aqp3 0.35aquaporin 8 NM_007474 Aqp8 0.33ATPase, Ca++ transporting, plasma membrane 4 NM_213616 Atp2b4 0.1ATPase, Na+/K+ transporting, alpha 1 polypeptide NM_144900 Atp1a1 0.39calcium channel, voltage-dependent, gamma subunit 6 NM_133183 Cacng6 0.41Chloride intracellular channel 5 NM_172621 Clic5 0.16fatty acid binding protein 6, ileal (gastrotropin) NM_008375 Fabp6 0.31sodium channel, voltage-gated, type III, beta NM_153522 Scn3b 0.31


265


Ubiquitination and ProteolysisF-box and leucine-rich repeat protein 8 NM_015821 Fbxl8 0.43granzyme B NM_013542 Gzmb 0.41matrix metalloproteinase 24 NM_010808 Mmp24 0.38protease, serine, 19 (neuropsin) NM_008940 Prss19 0.48ubiquitin specific peptidase 16 NM_024258 Usp16 0.26Othersmelanoma antigen, family A, 5 NM_020018 Magea5 0.23hornerin NM_133698 Hrnr 0.39cystatin E/M NM_028623 Cst6 0.43

aGenes that were suppressed >2-fold by BHA only in small intestine of Nrf2 wild-type




bGenes that were suppressed >2-fold by BHA only in liver of Nrf2 wild-type mice but

not in liver of Nrf2 knockout mice compared with vehicle treatment at 3h. The relative



Table 2.3.

266

Table 3.1. Oligonucleotide primers used in quantitative real-time PCR (qRT-PCR)

267

GenBank Gene Symbol Gene Title SIT* Liver**AccessionCell AdhesionNM_009864 Cdh1 Cadherin 1 6.77XM_283264 Cdh10 cadherin 10 7.01NM_007664 Cdh2 cadherin 2 9.72XM_488510 Cspg2 chondroitin sulfate proteoglycan 2 2.72 2.82NM_009903 Cldn4 claudin 4 4.86NM_018777 Cldn6 claudin 6 2.32NM_031174 Dscam Down syndrome cell adhesion molecule (Dscam) 2.25NM_010103 Edil3 EGF-like repeats and discoidin I-like domains 3 9.2NM_008401 Itgam integrin alpha M 2.42NM_008405 Itgb2l integrin beta 2-like 9.64

Jam3 Junction adhesion molecule 3 2.2NM_007736 Col4a5 procollagen, type IV, alpha 5 2.54XM_139187 Pcdh9 protocadherin 9 2.33Apoptosis and Cell cycle controlXM_194020 Acvr1c activin A receptor, type IC 26.49NM_178655 Ank2 ankyrin 2, brain 17.4NM_153287 Axud1 AXIN1 up-regulated 1 2.71

Bcl2 B-cell leukemia/lymphoma 2 (Bcl2), transcript variant 1 2.68NM_009744 Bcl6 B-cell leukemia/lymphoma 6 2.02NM_207653 Cflar CASP8 and FADD-like apoptosis regulator 2.12NM_026373 Cdk2ap2 CDK2-associated protein 2 2.35XM_484088 Cdc27 cell division cycle 27 homolog (S. cerevisiae) 2.36NM_009862 Cdc45l cell division cycle 45 homolog (S. cerevisiae)-like 3.38NM_026201 Ccar1 cell division cycle and apoptosis regulator 1 9.84NM_013538 Cdca3 cell division cycle associated 3 2.18NM_011806 Dmtf1 cyclin D binding myb-like transcription factor 1 2.88NM_028399 Ccnt2 cyclin T2 9.26NM_009874 Cdk7 cyclin-dependent kinase 7 (homolog of Xenopus MO15 cdk-activating kinase) 15.94NM_007837 Ddit3 DNA-damage inducible transcript 3 13.72 9NM_007950 Ereg epiregulin 5.85NM_008087 Gas2 Growth arrest specific 2 2.01XM_137276 Gas2l3 growth arrest-specific 2 like 3 4.26NM_146071 Muc20 mucin 20 2.96NM_009044 Rel reticuloendotheliosis oncogene 2.35NM_133810 Stk17b serine/threonine kinase 17b (apoptosis-inducing) 2.27NM_028769 Syvn1 synovial apoptosis inhibitor 1, synoviolin 4.77NM_021897 Trp53inp1 transformation related protein 53 inducible nuclear protein 1 2.49Biosynthesis and Metabolism

--- Acyl-CoA synthetase long-chain family member 5 17.14NM_029901 Akr1c21 aldo-keto reductase family 1, member C21 2.14NM_023179 Atp6v1g2 ATPase, H+ transporting, V1 subunit G isoform 2 2.231451144_at Bxdc2 brix domain containing 2 2.19NM_023525 Cad carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase 2.4NM_198415 Ckmt2 creatine kinase, mitochondrial 2 29.85NM_007710 Ckm creatine kinase, muscle 21.08NM_030225 Dlst dihydrolipoamide S-succinyltransferase (E2 component of 2-oxo-glutarate complex) 3.28NM_021896 Gucy1a3 guanylate cyclase 1, soluble, alpha 3 5.6NM_011846 Mmp17 matrix metallopeptidase 17 24.22NM_138656 Mvd mevalonate (diphospho) decarboxylase 3.46NM_009127 Scd1 stearoyl-Coenzyme A desaturase 1 2.26

Table 3.2. TM-induced Nrf2-dependent genes in mouse small intestine and


268

GenBank Gene Symbol Gene Title SIT* Liver**AccessionCalcium homeostasisNM_013471 Anxa4 Annexin A4 5.2NM_009722 Atp2a2 ATPase, Ca++ transporting, cardiac muscle, slow twitch 2 2.65NM_023116 Cacnb2 Calcium channel, voltage-dependent, beta 2 subunit 14.13NM_009781 Cacna1c Calcium channel, voltage-dependent, L type, alpha 1C subunit 7.94NM_028231 Kcnmb2 potassium large conductance calcium-activated channel, subfamily M, beta member 2 4.62Cell Growth and DifferentiationNM_010111 Efnb2 ephrin B2 2.67NM_177390 Myo1d Myosin ID 2.68NM_145610 Ppan peter pan homolog (Drosophila) 2.04NM_021883 Tmod1 tropomodulin 1 2.7NM_009394 Tnnc2 troponin C2, fast 16.76ER/Golgi transport and ER/Golgi biosynthesis/metabolismNM_025445 Arfgap3 ADP-ribosylation factor GTPase activating protein 3 2.58NM_025505 Blzf1 basic leucine zipper nuclear factor 1 7.72NM_009938 Copa coatomer protein complex subunit alpha 2.4NM_025673 Golph3 Golgi phosphoprotein 3 2.57NM_146133 Golph3l golgi phosphoprotein 3-like 2.41NM_008408 Itm1 intergral membrane protein 1 6.62 2.56NM_027400 Lman1 Lectin, mannose-binding 2.79NM_025408 Phca phytoceramidase, alkaline 3.04NM_009178 Siat4c ST3 beta-galactoside alpha-2,3-sialyltransferase 4 2.4NM_020283 B3galt1 UDP-Gal:betaGlcNAc beta 1,3-galactosyltransferase, polypeptide 1 2.54NM_011716 Wfs1 Wolfram syndrome 1 homolog (human) 2.02Electron TransportNM_015751 Abce1 ATP-binding cassette, sub-family E (OABP), member 1 2.48NM_010001 Cyp2c37 cytochrome P450, family 2. subfamily c, polypeptide 37 5.58NM_023913 Ern1 Endoplasmic reticulum (ER) to nucleus signalling 1 2.12XM_129326 Gucy2g guanylate cyclase 2g 2.39NM_007952 Pdia3 protein disulfide isomerase associated 3 3.11NM_009787 Pdia4 protein disulfide isomerase associated 4 3.19XM_907880 Pdia6 protein disulfide isomerase associated 6 2.9XM_284053 Steap2 six transmembrane epithelial antigen of prostate 2 3.23NM_198295 A730024F05RikThioredoxin domain containing 10 2.91NM_029572 Txndc4 thioredoxin domain containing 4 (endoplasmic reticulum) 2.47NM_023140 Txnl2 Thioredoxin-like 2 8.25G-protein coupled receptorsNM_008158 Gpr27 G protein-coupled receptor 27 2.72NM_145066 Gpr85 G protein-coupled receptor 85 3.78AK015353 Grm2 G protein-coupled receptor, family C, group 1, member B 2.18NM_008177 Grpr gastrin releasing peptide receptor 2.18NM_010314 Gngt1 guanine nucleotide binding protein (G protein), gamma transducing activity polypeptide 1 2.02NM_139270 Pthr2 parathyroid hormone receptor 2 2.12NM_011056 Pde4d phosphodiesterase 4D, cAMP specific 2.78NM_022881 Rgs18 regulator of G-protein signaling 18 2.36Kinases and PhosphatasesNM_153066 Ak5 adenylate kinase 5 2.33NM_144817 Camk1g calcium/calmodulin-dependent protein kinase I gamma 2.32NM_139059 Csnk1d Casein kinase 1, delta (Csnk1d), transcript variant 2 2.08NM_177914 MGI:3580254 diacylglycerol kinase kappa 13.2NM_130447 Dusp16 dual specificity phosphatase 16 3.17 2.13NM_019987 Ick intestinal cell kinase 2.06


269

GenBank Gene Symbol Gene Title SIT* Liver**AccessionXM_283179 Mast4 microtubule associated serine/threonine kinase family member 4 3.62NM_016700 Mapk8 mitogen activated protein kinase 8 12.43

Mapk8 mitogen activated protein kinase 8 7.41NM_172688 Map3k7 mitogen activated protein kinase kinase kinase 7 3.29NM_011101 Prkca Protein kinase C, alpha 2.04NM_011104 Prkce protein kinase C, epsilon 3.3NM_021880 Prkar1a protein kinase, cAMP dependent regulatory, type I, alpha 15.23NM_175638 Prkwnk4 Protein kinase, lysine deficient 4 8.16NM_016979 Prkx protein kinase, X-linked 2.27NM_133485 Ppp1r14c Protein phosphatase 1, regulatory (inhibitor) subunit 14c 5.03NM_012024 Ppp2r5e protein phosphatase 2, regulatory subunit B (B56), epsilon isoform 2.49NM_008913 Ppp3ca Protein phosphatase 3, catalytic subunit, alpha isoform 14.5AK134422 Ptp Protein tyrosine phosphatase 3.27NM_028259 Rps6kb1 ribosomal protein S6 kinase, polypeptide 1 2.05NM_031880 Tnk1 tyrosine kinase, non-receptor, 1 2.54Nuclear Assembly and ProcessingNM_010613 Khsrp KH-type splicing regulatory protein 2.44NM_008671 Nap1l2 nucleosome assembly protein 1-like 2 4.7NM_026175 Sf3a1 splicing factor 3a, subunit 1 2.77NM_009408 Top1 Topoisomerase (DNA) I 5.94NM_008717 Zfml Zinc finger, matrin-like 2.19Glucose biosynthesis/metabolismNM_009605 Adipoq adiponectin, C1Q and collagen domain containing 2.23NM_018763 Chst2 Carbohydrate sulfotransferase 2 2.02NM_008079 Galc galactosylceramidase 2.95NM_029626 Glt8d1 glycosyltransferase 8 domain containing 1 2.17NM_013820 Hk2 hexokinase 2 2.46NM_010705 Lgals3 Lectin, galactose binding, soluble 3 3.2NM_199446 Phkb phosphorylase kinase beta 2.06NM_016752 Slc35b1 solute carrier family 35, member B1 2.13Signaling molecules and interacting partnersNM_029291 Ascc2 Activating signal cointegrator 1 complex subunit 2 3.24NM_007498 Atf3 activating transcription factor 3 8.73NM_016707 Bcl11a B-cell CLL/lymphoma 11A (zinc finger protein) 4.2NM_033601 Bcl3 B-cell leukemia/lymphoma 3 2.08NM_007553 Bmp2 bone morphogenetic protein 2 2.55NM_007558 Bmp8a bone morphogenetic protein 8a 2.39NM_178661 Creb3l2 cAMP responsive element binding protein 3-like 2 2.01NM_010016 Daf1 decay accelerating factor 1 2.29NM_007897 Ebf1 early B-cell factor 1 8.98NM_023580 Epha1 Eph receptor A1 2.037NM_133753 Errfi1 ERBB receptor feedback inhibitor 1 2.53NM_00100586 Erbb2ip Erbb2 interacting protein 2.11NM_00100586 Erbb2ip Erbb2 interacting protein 2.04NM_007906 Eef1a2 eukaryotic translation elongation factor 1 alpha 2 3.67NM_007917 Eif4e eukaryotic translation initiation factor 4E 3.05NM_173363 Eif5 eukaryotic translation initiation factor 5 2.13NM_010515 Igf2r Insulin-like growth factor 2 receptor 2.22NM_010591 Jun Jun oncogene 2.29NM_010592 Jund1 Jun proto-oncogene related gene d1 2.43NM_008416 Junb Jun-B oncogene 2.36NM_013602 Mt1 metallothionein 1 2.15


270

GenBank Gene Symbol Gene Title SIT* Liver**AccessionNM_008630 Mt2 metallothionein 2 2.79NM_170671 Mycbpap Mycbp associated protein 3.97NM_177619 Myst2 MYST histone acetyltransferase 2 2NM_009123 Nkx1-2 NK1 transcription factor related, locus 2 (Drosophila) 2.45NM_030612 Nfkbiz nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, zeta 2.67NM_017373 Nfil3 nuclear factor, interleukin 3, regulated 12.68 3.34NM_144892 Ncoa5 nuclear receptor coactivator 5 14.65NM_173440 Nrip1 nuclear receptor interacting protein 1 2.87BC032981 Nfxl1 nuclear transcription factor, X-box binding-like 1 3.13NM_020005 Pcaf P300/CBP-associated factor 2.33NM_027924 Pdgfd platelet-derived growth factor, D polypeptide 6.17NM_017463 Pbx2 pre B-cell leukemia transcription factor 2 2.84NM_026383 Pnrc2 proline-rich nuclear receptor coactivator 2 2.08NM_145495 Rin1 Ras and Rab interactor 1 7.01 2.87NM_011651 Stk22s1 serine/threonine kinase 22 substrate 1 2.36NM_175246 Snip1 Smad nuclear interacting protein 1 2.07NM_007707 Socs3 suppressor of cytokine signaling 3 2.45NM_080843 Socs4 suppressor of cytokine signaling 4 2NM_009365 Tgfb1i1 transforming growth factor beta 1 induced transcript 1 2.22NM_00101302 Tgfbrap1 transforming growth factor, beta receptor associated protein 1 2.7NM_013869 Tnfrsf19 tumor necrosis factor receptor superfamily, member 19 2.4NM_010755 Maff v-maf musculoaponeurotic fibrosarcoma oncogene family, protein F (avian) 2.92NM_009524 Wnt5a wingless-related MMTV integration site 5A 8.25TransportNM_007511 Atp7b ATPase, Cu++ transporting, beta polypeptide 2.34NM_011075 Abcb1b ATP-binding cassette, sub-family B (MDR/TAP), member 1B 4.65NM_008576 Abcc1 ATP-binding cassette, sub-family C (CFTR/MRP), member 1 2.37NM_172621 Clic5 Chloride intracellular channel 5, mRNA 2.39NM_024406 Fabp4 Fatty acid binding protein 4, adipocyte 3.7NM_146188 Kctd15 potassium channel tetramerisation domain containing 15 2.82

Kctd7 potassium channel tetramerisation domain containing 7 2.65NM_148938 Slc1a3 solute carrier family 1 (glial high affinity glutamate transporter), member 3 4.03NM_019481 Slc13a1 solute carrier family 13 (sodium/sulphate symporters), member 1 2.09NM_00100414 Slc13a5 solute carrier family 13 (sodium-dependent citrate transporter), member 5 6.88NM_011395 Slc22a3 solute carrier family 22 (organic cation transporter), member 3 2.07NM_172980 Slc28a2 solute carrier family 28 (sodium-coupled nucleoside transporter), member 2 2NM_078484 Slc35a2 solute carrier family 35 (UDP-galactose transporter), member 2 6.5NM_011990 Slc7a11 solute carrier family 7 (cationic amino acid transporter, y+ system), member 11 5.93NM_080852 Slc7a12 solute carrier family 7 (cationic amino acid transporter, y+ system), member 12 2.95NM_011406 Slc8a1 Solute carrier family 8 (sodium/calcium exchanger), member 1 2.86NM_178892 Tiparp TCDD-inducible poly(ADP-ribose) polymerase 2.27Ubiquitination and ProteolysisNM_027926 Cpa4 carboxypeptidase A4 2.29NM_011931 Cop1 Constitutive photomorphogenic protein 2.52NM_013868 Hspb7 heat shock protein family, member 7 (cardiovascular) 2.25NM_146042 Ibrdc2 IBR domain containing 2 6.77NM_009174 Siah2 seven in absentia 2 2.46

Siah2 seven in absentia 2 2.12NM_025692 Ube1dc1 ubiquitin-activating enzyme E1-domain containing 1 2.79NM_173443 Vcpip1 valosin containing protein (p97)/p47 complex interacting protein 1 2.12Molecular chaperones and Heat Shock ProteinsNM_00101240 Hspb6 heat shock protein, alpha-crystallin-related, B6 2.13


271

GenBank Gene Symbol Gene Title SIT* Liver**AccessionNM_010918 Nktr natural killer tumor recognition sequence 2.02NM_030201 Stch stress 70 protein chaperone, microsome-associated, human homolog 2.47

Stch stress 70 protein chaperone, microsome-associated, human homolog 2.37MiscellaneousNM_008161 Gpx3 glutathione peroxidase 3 5.79NM_028733 Pacsin3 Protein kinase C and casein kinase II substrate 3 (Pacsin3) 2.12NM_009409 Top2b Topoisomerase (DNA) II beta (Top2b), mRNA 3.39NM_020283 B3galt1 UDP-Gal:betaGlcNAc beta 1,3-galactosyltransferase, polypeptide 1 2.45

*Genes that were induced >2-fold by TM only in small intestine of Nrf2 wild-type mice

but not in small intestine of Nrf2 knockout mice compared with vehicle treatment at 3h.

The relative mRNA expression levels of each gene in treatment group over vehicle

group (fold changes) are listed.

**Genes that were induced >2-fold by TM only in liver of Nrf2 wild-type mice but not

in liver of Nrf2 knockout mice compared with vehicle treatment at 3h. The relative



Table 3.2.

272

GenBank Gene SymbolGene Title SIT* Liver**AccessionCell AdhesionNM_174988 Cdh22 cadherin 22 0.47NM_053096 Cml2 camello-like 2 0.45NM_009818 Catna1 Catenin (cadherin associated protein), alpha 1 0.13NM_008729 Catnd2 Catenin (cadherin associated protein), delta 2 0.5XM_488510 Cspg2 chondroitin sulfate proteoglycan 2 0.43NM_018764 Pcdh7 protocadherin 7 0.21NM_053134 Pcdhb9 protocadherin beta 9 0.28NM_033595 Pcdhga12 Protocadherin gamma subfamily A, 10, mRNA 0.4Apoptosis and Cell cycle controlNM_007566 Birc6 baculoviral IAP repeat-containing 6 0.49NM_009741 Bcl2 B-cell leukemia/lymphoma 2 0.25NM_009950 Cradd CASP2 and RIPK1 domain containing adaptor with death domain 0.43NM_007609 Casp11 caspase 11, apoptosis-related cysteine peptidase 0.45NM_009811 Casp6 caspase 6 0.36NM_025680 Ctnnbl1 catenin, beta like 1 0.45NM_025866 Cdca7 cell division cycle associated 7 0.36NM_026560 Cdca8 cell division cycle associated 8 0.37XM_181420 Cgref1 cell growth regulator with EF hand domain 1 0.47NM_009131 Clec11a C-type lectin domain family 11, member a 0.2NM_146207 Cul4a cullin 4A 0.49NM_009873 Cdk6 cyclin-dependent kinase 6 0.47NM_009876 Cdkn1c cyclin-dependent kinase inhibitor 1C (P57) 0.48NM_007892 E2f5 E2F transcription factor 5 0.25NM_008655 Gadd45b growth arrest and DNA-damage-inducible 45 beta 0.18NM_183358 Gadd45gip1 growth arrest and DNA-damage-inducible, gamma interacting protein 1 0.45NM_010578 Itgb1 integrin beta 1 (fibronectin receptor beta) 0.12NM_019745 Pdcd10 programmed cell death 10 0.46NM_009383 Tial1 Tial1 cytotoxic granule-associated RNA binding protein-like 1 0.07 0.36NM_009425 Tnfsf10 tumor necrosis factor (ligand) superfamily, member 10 0.33NM_009517 Wig1 wild-type p53-induced gene 1 0.5Biosynthesis and MetabolismNM_177470 Acaa2 acetyl-Coenzyme A acyltransferase 2 (mitochondrial 3-oxoacyl-Coenzyme A thiolase) 0.49NM_133904 Acacb Acetyl-Coenzyme A carboxylase beta 0.14NM_009695 Apoc2 apolipoprotein C-II 0.41NM_010174 Fabp3 Fatty acid binding protein 3, muscle and heart 0.23NM_008609 Mmp15 matrix metallopeptidase 15 0.46NM_023792 Pank1 pantothenate kinase 1 0.29 0.3NM_144844 Pcca propionyl-Coenzyme A carboxylase, alpha polypeptide 0.49NM_013743 Pdk4 pyruvate dehydrogenase kinase, isoenzyme 4 0.4NM_019437 Rfk riboflavin kinase 0.48NM_138758 Tmlhe trimethyllysine hydroxylase, epsilon 0.37NM_133995 Upb1 ureidopropionase, beta 0.42NM_009471 Umps uridine monophosphate synthetase 0.36Calcium homeostasisNM_013472 Anxa6 annexin A6 0.43NM_007590 Calm3 calmodulin 3 0.43NM_023051 Clstn1 calsyntenin 1 0.5Electron TransportXM_485295 Cyb561d1 cytochrome b-561 domain containing 1 0.44NM_013809 Cyp2g1 cytochrome P450, family 2, subfamily g, polypeptide 1 0.43

Table 3.3. TM-suppressed Nrf2-dependent genes in mouse small intestine and


273

GenBank Gene SymbolGene Title SIT* Liver**AccessionNM_177380 Cyp3a44 cytochrome P450, family 3, subfamily a, polypeptide 44 0.39NM_018887 Cyp39a1 cytochrome P450, family 39, subfamily a, polypeptide 1 0.41NM_010012 Cyp8b1 cytochrome P450, family 8, subfamily b, polypeptide 1 0.5NM_170778 Dpyd dihydropyrimidine dehydrogenase 0.49NM_010231 Fmo1 flavin containing monooxygenase 1 0.42NM_008631 Mt4 metallothionein 4 0.13NM_026614 Ndufa5 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 5 0.4NM_026610 Ndufb4 NADH dehydrogenase (ubiquinone) 1 beta subcomplex 4 0.47NM_010887 Ndufs4 NADH dehydrogenase (ubiquinone) Fe-S protein 4 0.47NM_178239 Ndor1 NADPH dependent diflavin oxidoreductase 1 0.44XM_128552 Pdia2 protein disulfide isomerase associated 2 0.43NM_025848 Sdhd succinate dehydrogenase complex, subunit D, integral membrane protein 0.45NM_013711 Txnrd2 thioredoxin reductase 2 0.4NM_011743 Zfp106 zinc finger protein 106 0.1Golgi assembly and glycosylationNM_007454 Ap1b1 adaptor protein complex AP-1, beta 1 subunit 0.49NM_028758 Gga2 Golgi associated, gamma adaptin ear containing, ARF binding protein 2 0.44NM_008315 St3gal2 ST3 beta-galactoside alpha-2,3-sialyltransferase 2 0.5G-protein coupled receptorsNM_008315 Htr7 5-hydroxytryptamine (serotonin) receptor 7 (Htr7), mRNA 0.47NM_177231 Arrb1 arrestin, beta 1 0.38NM_030258 Gpr146 G protein-coupled receptor 146 0.49NM_010309 Gnas GNAS (guanine nucleotide binding protein, alpha stimulating) complex locus 0.38NM_023121 Gngt2 guanine nucleotide binding protein (G protein), gamma transducing activity polypeptide 2 0.47NM_008142 Gnb1 guanine nucleotide binding protein, beta 1 0.5NM_053235 V1rc5 vomeronasal 1 receptor, C5 0.43Kinases and PhosphatasesNM_177343 Camk1d Calcium/calmodulin-dependent protein kinase 1D 0.18NM_009793 Camk4 Calcium/calmodulin-dependent protein kinase IV (Camk4) 0.11NM_013642 Dusp1 dual specificity phosphatase 1 0.44NM_010765 Mapkapk5 MAP kinase-activated protein kinase 5 0.47NM_011951 Mapk14 mitogen activated protein kinase 14 0.46NM_011944 Map2k7 mitogen activated protein kinase kinase 7 0.16NM_023538 Mulk multiple substrate lipid kinase 0.45NM_145962 Pank3 pantothenate kinase 3 0.4NM_001024955 Pik3r1 phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 1 (p85 alpha) 0.46NM_145401 Prkag2 protein kinase, AMP-activated, gamma 2 non-catalytic subunit 0.49 0.12NM_017374 Ppp2cb Protein phosphatase 2a, catalytic subunit, beta isoform 0.44NM_008914 Ppp3cb protein phosphatase 3, catalytic subunit, beta isoform 0.44NM_019651 Ptpn9 Protein tyrosine phosphatase, non-receptor type 9 0.23NM_011213 Ptprf protein tyrosine phosphatase, receptor type, F 0.4NM_009184 Ptk6 PTK6 protein tyrosine kinase 6 0.37NM_013845 Ror1 Receptor tyrosine kinase-like orphan receptor 1 0.43NM_019924 Rps6ka4 ribosomal protein S6 kinase, polypeptide 4 0.43Nuclear assembly and processingNM_148948 Dicer1 Dicer1, Dcr-1 homolog (Drosophila) 0.43XM_131040 Hist2h2bb Histone 2, H2bb 0.37NM_019786 Tbk1 TANK-binding kinase 1 0.4Glucose biosynthesis/metabolismNM_019395 Fbp1 fructose bisphosphatase 1 0.47NM_025799 Fuca2 fucosidase, alpha-L- 2, plasma 0.44


274

GenBank Gene SymbolGene Title SIT* Liver**AccessionNM_008155 Gpi1 glucose phosphate isomerase 1 0.49NM_008061 G6pc glucose-6-phosphatase, catalytic 0.26NM_001013374 Lman2l lectin, mannose-binding 2-like 0.49NM_008548 Man1a mannosidase 1, alpha 0.45NM_010956 Ogdh Oxoglutarate dehydrogenase (lipoamide) 0.25NM_001013367 Prkaa1 protein kinase, AMP-activated, alpha 1 catalytic subunit 0.49Signaling molecules and interacting partnersNM_009755 Bmp1 bone morphogenetic protein 1 0.42NM_001013368 E2f8 E2F transcription factor 8 0.41NM_010141 Epha7 Eph receptor A7 0.49NM_020273 Gmeb1 glucocorticoid modulatory element binding protein 1 0.48NM_010323 Gnrhr Gonadotropin releasing hormone receptor 0.28NM_176958 Hif1an hypoxia-inducible factor 1, alpha subunit inhibitor 0.49NM_010547 Ikbkg inhibitor of kappaB kinase gamma 0.45NM_010515 Igf2r insulin-like growth factor 2 receptor 0.44NM_010513 Igf1r insulin-like growth factor I receptor 0.35 0.5NM_009697 Nr2f2 Nuclear receptor subfamily 2, group F, member 2 0.45

Pdap1 PDGFA associated protein 1 0.47NM_013634 Pparbp peroxisome proliferator activated receptor binding protein 0.44NM_027230 Prkcbp1 protein kinase C binding protein 1 0.48NM_026880 Pink1 PTEN induced putative kinase 1 0.48NM_018773 Scap2 src family associated phosphoprotein 2 0.49NM_007706 Socs2 Suppressor of cytokine signaling 2 0.44NM_178111 Trp53inp2 tumor protein p53 inducible nuclear protein 2 0.47NM_011703 Vipr1 vasoactive intestinal peptide receptor 1 0.4NM_010153 Erbb3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) 0.46TransportNM_009727 Atp8a1 ATPase, aminophospholipid transporter (APLT), class I, type 8A, member 1 0.44NM_024173 Atp6v1g1 ATPase, H+ transporting, V1 subunit G isoform 1 0.46NM_029600 Abcc3 Multidrug resistance-associated protein 3 (Abcc3) 0.46NM_010604 Kcnj16 potassium inwardly-rectifying channel, subfamily J, member 16 0.15NM_018824 Slc23a2 Sodium-dependent vitamin C transporter type 2 (Slc23a1) 0.45NM_011397 Slc23a1 solute carrier family 23 (nucleobase transporters), member 1 0.3NM_008063 Slc37a4 solute carrier family 37 (glycerol-6-phosphate transporter), member 4 0.46NM_018760 Slc4a4 solute carrier family 4 (anion exchanger), member 4 0.5NM_016917 Slc40a1 Solute carrier family 40 (iron-regulated transporter), member 1 0.45XM_127434 Slc9a3 solute carrier family 9 (sodium/hydrogen exchanger), member 3 0.41Detoxifying enzymesNM_008129 Gclm glutamate-cysteine ligase , modifier subunit 0.45NM_029555 Gstk1 glutathione S-transferase kappa 1 0.45NM_010357 Gsta4 glutathione S-transferase, alpha 4 0.49NM_010359 Gstm3 glutathione S-transferase, mu 3 0.22XM_359308 Gstm7 glutathione S-transferase, mu 7 0.46NM_008185 Gstt1 glutathione S-transferase, theta 1 0.43NM_025304 Lcmt1 leucine carboxyl methyltransferase 1 0.48NM_019946 Mgst1 microsomal glutathione S-transferase 1 0.18NM_025569 Mgst3 microsomal glutathione S-transferase 3 0.47NM_019878 Sult1b1 sulfotransferase family 1B, member 1 0.41Ubiquitination and ProteolysisNM_011780 Adam23 A disintegrin and metallopeptidase domain 23 0.34NM_007754 Cpd carboxypeptidase D 0.45


275

GenBank Gene SymbolGene Title SIT* Liver**AccessionNM_134015 Fbxw11 F-box and WD-40 domain protein 11 0.27NM_177703 Fbxw19 F-box and WD-40 domain protein 19 0.11NM_028705 Herc3 hect domain and RLD 3 0.35NM_145486 Mar 2 membrane-associated ring finger (C3HC4) 2 0.5 0.28NM_020487 Prss21 protease, serine, 21 0.12NM_008944 Psma2 proteasome (prosome, macropain) subunit, alpha type 2 0.49NM_013640 Psmb10 proteasome (prosome, macropain) subunit, beta type 10 0.5XM_483996 Usp34 ubiquitin specific peptidase 34 0.49NM_013918 Usp25 Ubiquitin-specific processing protease 0.47Molecular Chaperones and Heat Shock ProteinsNM_146036 Ahsa1 AHA1, activator of heat shock 90kDa protein ATPase homolog 1 (yeast) 0.4NM_025384 Dnajc15 DnaJ (Hsp40) homolog, subfamily C, member 15 0.5NM_139139 Dnajc17 DnaJ (Hsp40) homolog, subfamily C, member 17 0.43NM_024219 Hsbp1 heat shock factor binding protein 1 0.46

Hspa1b heat shock protein 1B 0.41NM_019960 Hspb3 heat shock protein 3 0.3MiscellaneousNM_008708 Nmt2 N-myristoyltransferase 2 0.14NM_007453 Prdx6 peroxiredoxin 6 0.46NM_011434 Sod1 Superoxide dismutase 1, soluble 0.25

*Genes that were suppressed >2-fold by TM only in small intestine of Nrf2 wild-type




**Genes that were suppressed >2-fold by TM only in liver of Nrf2 wild-type mice but

not in liver of Nrf2 knockout mice compared with vehicle treatment at 3h. The relative



Table 3.3.

276

Table 4.1.Oligonucleotide primers used for quantitative real-time PCR (qRT-PCR)

277

Table 4.2. Temporal gene expression profiles elicited by combinations of SFN and

EGCG

HT-29 AP-1 cells were treated for 6 hr or 10 hr with individual dietary factors or

combinations of SFN and EGCG as indicated. RNA was extracted, transcribed into

278

cDNA after ascertaining RNA integrity, and quantitative real-time PCR assays were

performed using beta-actin as the housekeeping gene. Values represent mean ± standard

deviation for three replicates of each gene, and are representative of two independent

experiments.

279

Table 4.3. Temporal gene expression profiles elicited by combinations of SFN and

EGCG in the presence of SOD

HT-29 AP-1 cells were co-treated for 6 hr or 10 hr with 20U/ml SOD and individual

dietary factors or combinations of SFN and EGCG as indicated. RNA was extracted,

280

transcribed into cDNA after ascertaining RNA integrity, and quantitative real-time PCR

assays were performed using beta-actin as the housekeeping gene. Values represent

mean ± standard deviation for three replicates of each gene, and are representative of

two independent experiments.

281

Matrix Family Matrix Family Family InformationHuman Murine

Nrf2 vs. AP-1 Nrf2 vs. AP-1V$AP4R V$AP4R Activator protein 4 and related proteinsV$CDEF - Cell cycle regulators: Cell cycle dependent elementV$CHRF - Cell cycle regulators: Cell cycle homology element

- V$CP2F CP2-erythrocyte Factor related to drosophila Elf1V$E2FF - E2F-myc activator/cell cycle regulator

- V$E4FF Ubiquitous GLI - Krueppel like zinc finger involved in cell cycle regulationV$EBOX - E-box binding factorsV$EGRF V$EGRF EGR/nerve growth factor induced protein C & related factors

- V$EKLF Basic and erythroid krueppel like factors- V$ETSF Human and murine ETS1 factors

V$EVI1 - EVI1-myleoid transforming protein- V$FKHD Fork head domain factors

V$GATA - GATA binding factors- V$GLIF GLI zinc finger family- V$GREF Glucocorticoid responsive and related elements

V$HAND - bHLH transcription factor dimer of HAND2 and E12- V$HESF Vertebrate homologues of enhancer of split complex- V$HIFF Hypoxia inducible factor, bHLH/PAS protein family- V$HNF6 Onecut homeodomain factor HNF6- V$INSM Insulinoma associated factors

V$MAZF V$MAZF Myc associated zinc fingers- V$MOKF Mouse Krueppel like factor

V$MYBL - Cellular and viral myb-like transcriptional regulatorsV$MYOD V$MYOD Myoblast determining factorsV$NEUR - NeuroD, Beta2, HLH domainV$NFKB - Nuclear factor kappa B/c-relV$NRF1 V$NRF1 Nuclear respiratory factor 1V$PAX5 - PAX-5 B-cell-specific activator proteinV$PAX9 - PAX-9 binding sitesV$PBXC - PBX1 - MEIS1 complexes

- V$PLAG Pleomorphic adenoma gene- V$SF1F Vertebrate steroidogenic factor

V$SP1F V$SP1F GC-Box factors SP1/GC- V$SRFF Serum response element binding factor

V$STAF - Selenocysteine tRNA activating factorV$STAT - Signal transducer and activator of transcription

- V$XBBF X-box binding factorsV$ZBPF V$ZBPF Zinc binding protein factors

- V$ZF35 Zinc finger protein ZNF35

Table 5.1. A. Human and Murine Matrix Families conserved between Nrf2 and

AP-1

282

Matrix Family Matrix Family Family InformationHuman Murine

ATF2 vs. ELK1 Atf2 vs. Elk1V$AP1F - AP1, Activator protein 1

- V$BARB Barbiturate-inducible element box from pro+eukaryotic genes- V$BNCF Basonuclein rDNA transcription factor (PolI)- V$BRNF Brn POU domain factors

V$CAAT - CCAAT binding factors- V$CLOX CLOX and CLOX homology (CDP) factors

V$COMP - Factors which cooperate with myogenic proteins- V$CP2F CP2-erythrocyte Factor related to drosophila Elf1

V$CREB - Camp-responsive element binding proteins- V$E2FF E2F-myc activator/cell cycle regulator

V$EBOX - E-box binding factorsV$EGRF - EGR/nerve growth factor induced protein C & related factorsV$EKLF - Basic and erythroid krueppel like factorsV$EREF - Estrogen response elementsV$ETSF V$ETSF Human and murine ETS1 factors

- V$EVI1 EVI1-myleoid transforming proteinV$FKHD V$FKHD Fork head domain factors

- V$FXRE Farnesoid X - activated receptor response elements- V$GATA GATA binding factors- V$GCMF Chorion-specific transcription factors with a GCM DNA binding domain

V$GFI1 - Growth factor independence transcriptional repressorV$GKLF V$GKLF Gut-enriched Krueppel like binding factorV$GLIF - GLI zinc finger family

- V$HAML Human acute myelogenous leukemia factors- V$HOXC HOX - PBX complexes- V$HOXF Factors with moderate activity to homeo domain consensus sequence- V$IKRS Ikaros zinc finger family

V$IRFF V$IRFF Interferon regulatory factorsV$LEFF V$LEFF LEF1/TCF, involved in the Wnt signal transduction pathwayV$MAZF - Myc associated zinc fingers

- V$MEF2 MEF2, myocyte-specific enhancer binding factorV$MYBL - Cellular and viral myb-like transcriptional regulators

- V$MYT1 MYT1 C2HC zinc finger proteinV$MZF1 - Myeloid zinc finger 1 factorsV$NBRE - NGFI-B response elements, nur subfamily of nuclear receptors

- V$NFAT Nuclear factor of activated T-cellsV$NFKB - Nuclear factor kappa B/c-relV$NKXH V$NKXH NKX homeodomain factorsV$NR2F V$NR2F Nuclear receptor subfamily 2 factors

- V$OCT1 Octamer binding proteinV$PARF V$PARF PAR/bZIP familyV$PAX5 - PAX-5 B-cell-specific activator proteinV$PAX6 - PAX-4/PAX-6 paired domain binding sitesV$PBXC - PBX1 - MEIS1 complexesV$PERO - Peroxisome proliferator-activated receptor

- V$PLZF C2H2 zinc finger protein PLZFV$RREB - Ras-responsive element binding proteinV$RXRF - RXR heterodimer binding sitesV$SNAP - snRNA-activating protein complexV$SP1F - GC-Box factors SP1/GC

- V$SRFF Serum response element binding factorV$STAF - Selenocysteine tRNA activating factor

- V$STAT Signal transducer and activator of transcriptionV$TALE - TALE homeodomain class recognizing TG motifs

- V$TBPF Tata-binding protein factor- V$TEAF TEA/ATTS DNA binding domain factors- V$XBBF X-box binding factors

V$ZBPF - Zinc binding protein factors

Table 5.1. B. Human and Murine Matrix Families conserved between ATF-2 and

ELK1

283

Table 5.2. Matrices with conserved regulatory sequences in promoter regions of

human, or murine, NRF2 and AP-1…..continued…

284


285


286


287


288


289

Table 5.2.

290

GenBank Gene Gene Prostate ProstateAccession No. Symbol Title 3hr* 12hr**

Apoptosis and cell cycleNM_019816 Aatf apoptosis antagonizing transcription factor - 3.09NM_007466 Api5 apoptosis inhibitor 5 - 7.26NM_007609 Casp4 caspase 4, apoptosis-related cysteine peptidase - 4.24NM_011997 Casp8ap2 caspase 8 associated protein 2 - 4.67 NM_026201 Ccar1 cell division cycle and apoptosis regulator 1 - 3.31 NM_027545 Cwf19l2 CWF19-like 2, cell cycle control (S. pombe) - 3.48

NM_001037134 Ccne2 cyclin E2 - 4.04 NM_009831 Ccng1 cyclin G1 - 3.82NM_028399 Ccnt2 cyclin T2 - 3.16NM_145991 Cdc73 Vcell division cycle 73, Paf1/RNA polymerase II complex component, - 6.13

homolog (S. cerevisiae)Calcium ion binding

NM_009722 Atp2a2 ATPase, Ca++ transporting, cardiac muscle, slow twitch 2 - 3.14NM_009784 Cacna2d1 calcium channel, voltage-dependent, alpha2/delta subunit 1 - 4.81NM_007977 F8 coagulation factor VIII - 4.18NM_007868 Dmd dystrophin, muscular dystrophy - 3.21 NM_007943 Eps15 epidermal growth factor receptor pathway substrate 15 - 5.26

NM_001039644 Edem3 ER degradation enhancer, mannosidase alpha-like 3 - 4.54NM_010427 Hgf hepatocyte growth factor - 4.49

XM_001472723 Macf1 microtubule-actin crosslinking factor 1 - 3.96 NM_011110 Pla2g5 phospholipase A2, group V - 3.89NM_013829 Plcb4 phospholipase C, beta 4 - 3.94 NM_009048 Reps1 RalBP1 associated Eps domain containing protein - 6.19NM_021450 Trpm7 transient receptor potential cation channel, subfamily M, member 7 - 4.46

Digestion NM_025583 Ctrb1 chymotrypsinogen B1 - 4.43NM_025469 Clps colipase, pancreatic - 3.85NM_009430 Prss2 protease, serine, 2 - 28.91

Extracellular spaceNM_009692 Apoa1 apolipoprotein A-I - 9.72 NM_018782 Calcrl calcitonin receptor-like - 5.1

NM_001042611 Cp ceruloplasmin - 3.77NM_019919 Ltbp1 latent transforming growth factor beta binding protein 1 - 3.46

NM_001039094 Negr1 neuronal growth regulator 1 - 3.15 NM_011964 Psg19 pregnancy specific glycoprotein 19 - 8.93NM_009936 Col9a3 procollagen, type IX, alpha 3 - 3.48

NM_001081385 Pcdh11x protocadherin 11 X-linked - 8.52NM_053141 Pcdhb16 protocadherin beta 16 - 3.17NM_021289 Smr2 submaxillary gland androgen regulated protein 2 - 29.55

Integral to Plasma MembraneNM_008309 Htr1d 5-hydroxytryptamine (serotonin) receptor 1D 10.02 -NM_008427 Kcnj4 potassium inwardly-rectifying channel, subfamily J, member 4 3.49 -NM_008422 Kcnc3 potassium voltage gated channel, Shaw-related subfamily, member 3 11.48 -NM_008434 Kcnq1 potassium voltage-gated channel, subfamily Q, member 1 3.34 -

NM_001098170 Pcdh10 protocadherin 10 4.1 -BC098457 Pcdhgc3 protocadherin gamma subfamily C, 3 3.5 -AK041751 Tcrb-J T-cell receptor beta, joining region 11.66 -

NM_029975 Ulbp1 UL16 binding protein 1 3.57 -

IntracellularNM_007478 Arf3 ADP-ribosylation factor 3 - 4.82NM_134037 Acly ATP citrate lyase - 4.49NM_015802 Dlc1 deleted in liver cancer 1 3.23 -NM_178118 Dixdc1 DIX domain containing 1 8.24 3.65 NM_007961 Etv6 ets variant gene 6 (TEL oncogene) 4.57 -NM_080433 Fezf2 Fez family zinc finger 2 - 3.44NM_053202 Foxp1 forkhead box P1 - 3.09

NM_175143 Qrich1 glutamine-rich 1 4.75 -NM_178888 Garnl3 GTPase activating RANGAP domain-like 3 3.02 -

NM_001025597 Ikzf1 IKAROS family zinc finger 1 13.8 -NM_011771 Ikzf3 IKAROS family zinc finger 3 - 5.44

Table 5.3. Temporal microarray analyses of genes suppressed by SFN+EGCG

combination in the prostate of NRF2-deficient mice…..continued…

291


NM_016889 Insm1 insulinoma-associated 1 4.07 -NM_029416 Klf17 Kruppel-like factor 17 3.25 -NM_053158 Mrpl1 mitochondrial ribosomal protein L1 - 7.61NM_031260 Mov10l1 Moloney leukemia virus 10-like 1 4.83 -NM_010823 Mpl myeloproliferative leukemia virus oncogene 4.77 -NM_031881 Nedd4l neural precursor cell expressed, developmentally 3.76 -

down-regulated gene 4-like NM_139144 Ogt O-linked N-acetylglucosamine (GlcNAc) transferase 3.02 -

(UDP-N-acetylglucosamine:polypeptide-N-acetylglucosaminyl transferase)AY033991 Plcd4 phospholipase C, delta 4 4.55 -

NM_008884 Pml promyelocytic leukemia 4.5 -NM_009391 Ran RAN, member RAS oncogene family - 3.96NM_011246 Rasgrp1 RAS guanyl releasing protein 1 3.43 -

NM_001081105 Rhoh ras homolog gene family, member H 5.73 -NM_145452 Rasa1 RAS p21 protein activator 1 - 3.31NM_172525 Arhgap29 Rho GTPase activating protein 29 - 5.05NM_015830 Solh small optic lobes homolog (Drosophila) 4.86 -NM_028004 Ttn titin 4.77 -NM_011529 Tank TRAF family member-associated Nf-kappa B activator - 6.11NM_009541 Zbtb17 zinc finger and BTB domain containing 17 5.46 -NM_008717 Zfml zinc finger, matrin-like 3.36 -

KinasesNM_134079 Adk Adenosine kinase - 4.07NM_007561 Bmpr2 bone morphogenic protein receptor, type II (serine/threonine kinase) - 6.14

NM_001025439 Camk2d calcium/calmodulin-dependent protein kinase II, delta - 4.6NM_001042634 Clk1 CDC-like kinase 1 - 3.61

NM_007714 Clk4 CDC like kinase 4 - 3.33NM_001109626 Crkrs Cdc2-related kinase, arginine/serine-rich - 7.33

NM_009974 Csnk2a2 Casein kinase 2, alpha prime polypeptide 6.07 -XM_979562 Etnk1 ethanolamine kinase 1 - 3.19NM_019827 Gsk3b Glycogen synthase kinase 3 beta - 4.47NM_010367 Magi1 Membrane associated guanylate kinase, WW and PDZ domain containing 1 3.12 -NM_015823 Magi2 Membrane associated guanylate kinase, WW and PDZ domain containing 2 6.15NM_009157 Map2k4 Mitogen activated protein kinase kinase 4 - 3.31NM_011946 Map3k2 Mitogen activated protein kinase kinase kinase 2 - 5.02NM_025609 Map3k7ip1 mitogen-activated protein kinase kinase kinase 7 interacting protein 1 3.04 3.93NM_008696 Map4k4 mitogen-activated protein kinase kinase kinase kinase 4 3.54 -NM_201519 Map4k5 Mitogen-activated protein kinase kinase kinase kinase 5 - 3.28NM_011161 Mapk11 mitogen-activated protein kinase 11 - 10.9 NM_172632 Mapk4 mitogen-activated protein kinase 4 3.51 -NM_015806 Mapk6 mitogen-activated protein kinase 6 3.53 - NM_010878 Nck1 Non-catalytic region of tyrosine kinase adaptor protein 1 - 4.24NM_010879 Nck2 Non-catalytic region of tyrosine kinase adaptor protein 2 3.38 -NM_021605 Nek7 NIMA (never in mitosis gene a)-related expressed kinase 7 - 3.13NM_008702 Nlk Nemo like kinase - 3.99NM_172783 Phka2 Phosphorylase kinase alpha 2 3.07 -NM_011083 Pik3c2a phosphatidylinositol 3-kinase, C2 domain containing, alpha polypeptide - 5.18NM_008839 Pik3ca phosphatidylinositol 3-kinase, catalytic, alpha polypeptide - 3.92NM_008862 Pkia protein kinase inhibitor, alpha - 3.28NM_008855 Prkcb1 protein kinase C, beta 1 - 3.34NM_029239 Prkcn protein kinase C, nu 3.93 -

NM_001013833 Prkg1 Protein kinase, cGMP-dependent, type I 3.31 -NM_007982 Ptk2 PTK2 protein tyrosine kinase 2 3.47 - NM_009184 Ptk6 PTK6 protein tyrosine kinase 6 4.66 -NM_009071 Rock1 Rho-associated coiled-coil containing protein kinase 1 - 4.71NM_009072 Rock2 Rho-associated coiled-coil containing protein kinase 2 - 4.42NM_148945 Rps6ka3 Ribosomal protein S6 kinase polypeptide 3 - 8.82 NM_028259 Rps6kb1 ribosomal protein S6 kinase, polypeptide 1 - 3.36NM_019635 Stk3 serine/threonine kinase 3 (Ste20, yeast homolog) - 3.03NM_144825 Taok1 TAO kinase 1 - 3.93

XM_001474897 Tnik TRAF2 and NCK interacting kinase 4.01 8.1NM_010633 Uhmk1 U2AF homology motif (UHM) kinase 1 3.19 -NM_030724 Uck2 Uridine-cytidine kinase 2 4.1 -NM_198703 Wnk1 WNK lysine deficient protein kinase 1 - 6.45


292


Metal ion binding NM_009530 Atrx alpha thalassemia/mental retardation syndrome X-linked homolog (human) - 4.12NM_013476 Ar androgen receptor 3.23 -NM_133738 Antxr2 anthrax toxin receptor 2 - 3.15AB088408 Atp6v0d1 ATPase, H+ transporting, lysosomal V0 subunit D1 6.95 -

NM_139294 Braf Braf transforming gene - 3.67NM_153788 Centb1 centaurin, beta 1 - 6.1NM_007805 Cyb561 cytochrome b-561 5.53 -NM_010006 Cyp2d9 cytochrome P450, family 2, subfamily d, polypeptide 9 3.28 -NM_027816 Cyp2u1 cytochrome P450, family 2, subfamily u, polypeptide 1 5.7 - NM_011935 Esrrg estrogen-related receptor gamma 3.85 - NM_025923 Fancl Fanconi anemia, complementation group L - 3.38 NM_053242 Foxp2 forkhead box P2 - 4.42NM_026148 Lims1 LIM and senescent cell antigen-like domains 1 - 4.31NM_008636 Mtf1 metal response element binding transcription factor 1 15.93 -NM_028757 Nebl nebulette 4.66 -NM_152229 Nr2e1 nuclear receptor subfamily 2, group E, member 1 11.85 -NM_030676 Nr5a2 nuclear receptor subfamily 5, group A, member 2 4.12 -NM_010264 Nr6a1 nuclear receptor subfamily 6, group A, member 1 10.31 -NM_026000 Psmd9 proteasome (prosome, macropain) 26S subunit, non-ATPase, 9 3.96 -NM_177167 Ppm1e protein phosphatase 1E (PP2C domain containing) 3.56 -

NM_009088 Rpo1-4 RNA polymerase 1-4 4.87 4.31NM_001103157 Steap2 six transmembrane epithelial antigen of prostate 2 - 3.76

NM_009380 Thrb thyroid hormone receptor beta - 4.67 NM_009371 Tgfbr2 transforming growth factor, beta receptor II 4.54 -

PhosphatasesNM_008960 Pten phosphatase and tensin homolog - 3.05NM_027892 Ppp1r12a protein phosphatase 1, regulatory (inhibitor) subunit 12A - 3.77NM_008913 Ppp3ca Protein phosphatase 3, catalytic subunit, alpha isoform - 9.33NM_182939 Ppp4r2 protein phosphatase 4, regulatory subunit 2 - 3.39 NM_011200 Ptp4a1 protein tyrosine phosphatase 4a1 - 3.11 NM_008979 Ptpn22 Protein tyrosine phosphatase, non-receptor type 22 (lymphoid) - 3.65

NM_001014288 Ptprd Protein tyrosine phosphatase, receptor type, D - 6.2NM_025760 Ptplad2 Protein tyrosine phosphatase-like A domain containing 2 - 4.42NM_130447 Dusp16 dual specificity phosphatase 16 - 5.97 NM_177730 Impad1 inositol monophosphatase domain containing 1 - 3.72NM_011210 Ptprc protein tyrosine phosphatase, receptor type, C 4.33 -

Transcription factors and interacting partnersAY902311 Atf2 Activating transcription factor 2 - 8.21 AY903215 Atf7ip2 activating transcription factor 7 interacting protein 2 - 9.83

NM_001025392 Bclaf1 BCL2-associated transcription factor 1 - 3.78NM_015826 Dmrt1 doublesex and mab-3 related transcription factor 1 - 6.96NM_009210 Hltf helicase-like transcription factor - 3.93 NM_033322 Lztfl1 leucine zipper transcription factor-like 1 - 4.08NM_172153 Lcorl ligand dependent nuclear receptor corepressor-like - 3.68 NM_183355 Pbx1 pre B-cell leukemia transcription factor 1 - 5.26

NM_001018042 Sp3 trans-acting transcription factor 3 - 3.65AM295492 Sp6 trans-acting transcription factor 6 - 32.91

NM_013685 Tcf4 Transcription factor 4 - 3.84 NM_178254 Tcfl5 transcription factor-like 5 (basic helix-loop-helix) - 4.6NM_016767 Batf basic leucine zipper transcription factor, ATF-like - 3.89

NM_001109661 Bach2 BTB and CNC homology 2 - 4.89NM_133828 Creb1 cAMP responsive element binding protein 1 - 5.33

NM_001005868 Erbb2ip Erbb2 interacting protein - 3.66 NM_008031 Fmr1 fragile X mental retardation syndrome 1 homolog - 3.96NM_010431 Hif1a hypoxia inducible factor 1, alpha subunit - 8.62NM_009951 Igf2bp1 insulin-like growth factor 2 mRNA binding protein 1 5.27 -NM_027910 Klhdc3 kelch domain containing 3 3.33 -NM_172154 Lcor ligand dependent nuclear receptor corepressor - 3.25

NM_001005863 Mtus1 mitochondrial tumor suppressor 1 3.29 - NM_001081445 Ncam1 neural cell adhesion molecule 1 3.08 -

AY050663 Nfat5 nuclear factor of activated T-cells 5 - 4.4NM_010903 Nfe2l3 nuclear factor, erythroid derived 2, like 3 (Nrf3) - 3.03

NM_001024205 Nufip2 nuclear fragile X mental retardation protein interacting protein 2 - 4.4 NM_010881 Ncoa1 nuclear receptor coactivator 1 3.55 3.36NM_172495 Ncoa7 nuclear receptor coactivator 7 - 4.46NM_011308 Ncor1 nuclear receptor co-repressor 1 - 5.37

NM_011544 Tcf12 Transcription factor 12 - 6.06


293


Transferases NM_144807 Chpt1 choline phosphotransferase 1 - 4.13NM_133869 Cept1 Choline/ethanolaminephosphotransferase 1 - 5.65NM_030225 Dlst dihydrolipoamide S-succinyltransferase (E2 component of - 4.04

2-oxo-glutarate complex)NM_028087 Gcnt3 glucosaminyl (N-acetyl) transferase 3, mucin type - 7.23NM_028108 Nat13 N-acetyltransferase 13 - 4.05NM_008708 Nmt2 N-myristoyltransferase 2 - 4.07 NM_027869 Pnpt1 polyribonucleotide nucleotidyltransferase 1 - 3.04NM_172627 Pggt1b protein geranylgeranyltransferase type I, beta subunit - 5.61

NM_183028 Pcmtd1 protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing - 7.36 NM_028604 Trmt11 tRNA methyltransferase 11 homolog (S. cerevisiae) - 3.67 NM_144731 Galnt7 UDP-N-acetyl-alpha-D-galactosamine: - 3.06

polypeptide N-acetylgalactosaminyltransferase 7NM_172829 St6gal2 beta galactoside alpha 2,6 sialyltransferase 2 - 3.22NM_009178 St3gal4 ST3 beta-galactoside alpha-2,3-sialyltransferase 4 - 8.11NM_011674 Ugt8a UDP galactosyltransferase 8A - 13.64

UbiquitinationXM_001478436 Fbxl17 F-box and leucine-rich repeat protein 17 - 3.86

NM_016736 Nub1 negative regulator of ubiquitin-like proteins 1 3.57 - NM_146003 Senp6 SUMO/sentrin specific peptidase 6 - 4.5NM_016723 Uchl3 ubiquitin carboxyl-terminal esterase L3 (ubiquitin thiolesterase) - 6.71 NM_173010 Ube3a ubiquitin protein ligase E3A - 3.12NM_009481 Usp9x ubiquitin specific peptidase 9, X chromosome - 3.47NM_152825 Usp45 ubiquitin specific petidase 45 - 3.09NM_023585 Ube2v2 ubiquitin-conjugating enzyme E2 variant 2 - 5.5NM_172300 Ube2z ubiquitin-conjugating enzyme E2Z (putative) 3.41 - NM_177327 Wwp1 WW domain containing E3 ubiquitin protein ligase 1 - 3.9

OthersNM_007462 Apc adenomatosis polyposis coli - 5.42 NM_021456 Ces1 carboxylesterase 1 4.09 -NM_021369 Chrna6 cholinergic receptor, nicotinic, alpha polypeptide 6 4.25 -NM_028870 Cltb clathrin, light polypeptide (Lcb) 22.37 -NM_016716 Cul3 cullin 3 - 3.2NM_010076 Drd1a dopamine receptor D1A 12.13 -

NM_001033360 Gpr101 G protein-coupled receptor 101 4.26 -NM_008211 H3f3b H3 histone, family 3B 3.47 -NM_008285 Hrh1 histamine receptor H 1 10.24 -NM_133892 Lao1 L-amino acid oxidase 1 3.39 -NM_020280 Magea4 melanoma antigen, family A, 4 22.06 -NM_175632 Oscar osteoclast associated receptor 3.36 -NM_009419 Tpst2 protein-tyrosine sulfotransferase 2 7.76 -NM_146255 Slc1a7 solute carrier family 1 (glutamate transporter), member 7 9.36 -NM_009206 Slc4a1ap solute carrier family 4 (anion exchanger), member 1, adaptor protein - 3.27NM_022025 Slc5a7 solute carrier family 5 (choline transporter), member 7 9.51 -NM_177909 Slc9a9 solute carrier family 9 (sodium/hydrogen exchanger), isoform 9 4.23 -NM_011506 Sucla2 succinate-Coenzyme A ligase, ADP-forming, beta subunit 5.02 -NM_009409 Top2b topoisomerase (DNA) II beta 3.3

* Relative mRNA expression levels of genes that were suppressed >3-fold by

SFN+EGCG combination in prostate of Nrf2 wild-type mice but not in prostate of Nrf2

knockout mice compared with vehicle treatment at 3 hr.

** Relative mRNA expression levels of genes that were suppressed >3-fold by

SFN+EGCG combination in prostate of Nrf2 wild-type mice but not in prostate of Nrf2

knockout mice compared with vehicle treatment at 12 hr.

Table 5.3.

294

Table 6.1. Microarray datasets bearing inflammation/injury or cancer signatures

All ‘Nair’ datasets are from the Kong Laboratory; all ‘Faden’ datasets are as defined by

descriptors detailed above at the PEPR resource; all other datasets are as defined by

descriptors detailed above at the ONCOMINE or GEO resources; EGCG,

295

epigallocatechin-3-gallate; SFN, sulforaphane; BHA; butylated hydroxyanisole; ER,

endoplasmic reticulum; TM, tunicamycin.

296

Matrix Family Matrix Family Matrix Family Family InformationConserved After Multiple

Human Murine Species AlignmentNRF2 vs. NFKB1 Nrf2 vs. Nfkb1 NRF2 vs. NFKB1

- V$AHRR - AHR-arnt heterodimers and AHR-related factors- - V$AIRE Autoimmune regulatory element binding factor- - V$AP1R MAF and AP1 related factors- V$AP2F - Activator protein 2

V$AP4R V$AP4R - Activator protein 4 and related proteins- - V$ATBF AT-binding transcription factor- V$BCL6 V$BCL6 POZ domain zinc finger expressed in B-Cells- - V$BRAC Brachyury gene, mesoderm developmental factor- - V$BRNF Brn POU domain factors- - V$CAAT CCAAT binding factors- - V$CART Cart-1 (cartilage homeoprotein 1)- - V$CDXF Vertebrate caudal related homeodomain protein

V$CEBP - - Ccaat/Enhancer Binding ProteinV$CHRF - V$CHRF Cell cycle regulators: Cell cycle homology element

- - V$CIZF CAS interating zinc finger protein- - V$CLOX CLOX and CLOX homology (CDP) factors- - V$COMP Factors which cooperate with myogenic proteins

V$CREB - V$CREB Camp-responsive element binding proteins- V$CTCF - CTCF and BORIS gene family, transcriptional regulators with 11 highly conserved zinc finger domains

V$E2FF V$E2FF - E2F-myc activator/cell cycle regulator- V$E4FF - Ubiquitous GLI - Krueppel like zinc finger involved in cell cycle regulation

V$EBOX V$EBOX - E-box binding factors- V$EGRF - EGR/nerve growth factor induced protein C & related factors

V$EKLF V$EKLF V$EKLF Basic and erythroid krueppel like factors- V$EREF - Estrogen response elements

V$ETSF V$ETSF - Human and murine ETS1 factors- - V$EVI1 EVI1-myleoid transforming protein

V$FKHD - V$FKHD Fork head domain factors- V$FXRE - Farnesoid X - activated receptor response elements

V$GATA V$GATA V$GATA GATA binding factorsV$GFI1 - V$GFI1 Growth factor independence transcriptional repressor

- - V$GREF Glucocorticoid responsive and related elements- - V$GRHL Grainyhead-like transcription factors

V$HAND - - bHLH transcription factor dimer of HAND2 and E12- V$HEAT V$HEAT Heat shock factors- V$HESF - Vertebrate homologues of enhancer of split complex- - V$HNF1 Hepatic Nuclear Factor 1- - V$HNF6 Onecut homeodomain factor HNF6- - V$HOMF Homeodomain transcription factors- - V$HOXC HOX - PBX complexes

V$HOXF V$HOXF V$HOXF Factors with moderate activity to homeo domain consensus sequence- V$IKRS - Ikaros zinc finger family- - V$IRFF Interferon regulatory factors

V$LEFF - V$LEFF LEF1/TCF, involved in the Wnt signal transduction pathway- - V$LHXF Lim homeodomain factors

V$MAZF V$MAZF - Myc associated zinc fingers- - V$MEF2 MEF2, myocyte-specific enhancer binding factor

V$MYBL - - Cellular and viral myb-like transcriptional regulatorsV$MYOD V$MYOD - Myoblast determining factors

- - V$MYT1 MYT1 C2HC zinc finger protein- V$MZF1 - Myeloid zinc finger 1 factors

V$NEUR - V$NEUR NeuroD, Beta2, HLH domain- - V$NF1F Nuclear factor 1- - V$NFAT Nuclear factor of activated T-cells- V$NFKB - Nuclear factor kappa B/c-rel- - V$NKX6 NK6 homeobox transcription factors- - V$NKXH NKX homeodomain factors

V$NR2F V$NR2F V$NR2F Nuclear receptor subfamily 2 factors- V$NRF1 - Nuclear respiratory factor 1- - V$OCT1 Octamer binding protein- - V$OCTP OCT1 binding factor (POU-specific domain)

V$P53F V$P53F - p53 tumor suppressor- - V$PARF PAR/bZIP family

V$PAX5 - - PAX-5 B-cell-specific activator protein- V$PAX6 V$PAX6 PAX-4/PAX-6 paired domain binding sites- - V$PBXC PBX1 - MEIS1 complexes- - V$PDX1 Pancreatic and intestinal homeodomain transcr. factor- - V$PERO Peroxisome proliferator-activated receptor- - V$PIT1 GHF-1 pituitary specific pou domain transcription factor- V$PLAG - Pleomorphic adenoma gene

V$PLZF - V$PLZF C2H2 zinc finger protein PLZF- - V$PRDF Positive regulatory domain I binding factor- - V$PTF1 Pancreas transcription factor 1, heterotrimeric transcription factor- - V$RBIT Regulator of B-Cell IgH transcription- - V$RUSH SWI/SNF related nucleophosphoproteins with a RING finger DNA binding motif

V$RXRF V$RXRF V$RXRF RXR heterodimer binding sites- - V$SATB Special AT-rich sequence binding protein

V$SF1F - V$SF1F Vertebrate steroidogenic factor- V$SMAD - Vertebrate SMAD family of transcription factors- - V$SNAP snRNA-activating protein complex

V$SORY V$SORY V$SORY SOX/SRY-sex/testis determining and related HMG box factorsV$SP1F V$SP1F V$SP1F GC-Box factors SP1/GC

- V$SRFF V$SRFF Serum response element binding factorV$STAT - V$STAT Signal transducer and activator of transcription

- - V$TALE TALE homeodomain class recognizing TG motifs- V$TBPF V$TBPF Tata-binding protein factor- V$WHNF - Winged helix binding sites

V$XBBF - - X-box binding factorsV$ZBPF V$ZBPF - Zinc binding protein factorsV$ZF35 - - Zinc finger protein ZNF35V$ZFHX - V$ZFHX Two-handed zinc finger homeodomain transcription factors

Table 6.2. Human and Murine Matrix Families conserved between Nrf2 and

Nfkb1

297

Nfe2l2 CNS Start CNS End %id location Length Score Chr Strand Start End

1 84.2 200.5 87.57human 4424 4568 - intergenic 144 2 - 177796698 177796842chimp 1559 12467 98.4 intergenic 2b - 182248910 182259818dog 4037 4426 78.9 intergenic 389 36 - 24010936 24011325mouse 3993 4131 79.6 intergenic 138 2 - 75470119 75470257rat 4002 4133 80 intergenic 131 3 - 58360804 58360935

2 79.9 801.25 93.25human 46618 47576 - intronic 958 2 - 177838892 177839850chimp 16433 72741 98 intronic 2b - 182263784 182320092dog 44082 45048 76.3 intronic 966 36 - 24050981 24051947mouse 49275 49911 73 intronic 636 2 - 75515401 75516037rat 48247 48892 72.3 intronic 645 3 - 58405049 58405694

3 81.6 746.5 94.04human 63951 64978 - intronic 1027 2 - 177856225 177857252chimp 16433 72741 98 intronic 2b - 182263784 182320092dog 63914 64953 78 intronic 1039 36 - 24070813 24071852mouse 69040 69553 74.9 intronic 513 2 - 75535166 75535679rat 68447 68854 75.5 intronic 407 3 - 58425249 58425656

4 82.8 962.25 98.86human 95699 97201 - intronic 1502 2 - 177887973 177889475chimp 93603 105622 98.3 intronic 2b - 182340954 182352973dog 94052 95563 80.9 intronic 1511 36 - 24100951 24102462mouse 99967 100384 76.4 intronic 417 2 - 75566093 75566510rat 103210 103629 75.7 intronic 419 3 - 58460012 58460431

5 82.8 418.75 89.74human 112361 112533 - intronic 172 2 - 177904635 177904807chimp 113121 117879 98.1 intronic 2b - 182360472 182365230dog 105905 106887 82.1 intronic 517 36 - 24112804 24113786mouse 115077 115617 74.8 intronic 540 2 - 75581203 75581743rat 119448 119894 76.1 intronic 446 3 - 58476250 58476696

Table 6.3. A. Multiple species alignment for Nfe2l2

CNS, Conserved Non-Coding Sequences; Chr, chromosome.

298

Nfkb1 CNS start CNS end %id location length score chr strand start end

1 78 491 86.58human 75272 75516 - intergenic 244 4 + 103675774 103675545rat 190119 189890 77 intergenic 229 2 - 233663290 233663525mouse 585736 585501 80 intergenic 235 3 - 135287512 135286504dog 52140 53148 78 intergenic 1008 32 + 26791395 26841008chimp 67709 117322 98 intergenic 49613 4 + 105654198 105654198



Table 6.3. B. Multiple species alignment for Nfkb1

CNS, Conserved Non-Coding Sequences; chr, chromosome.

299

Sr. No. GenBank Gene Name Gene Papaiahgari SFN+EGCG BHA Tunicamycin Faden Accession Symbol Lung injury and Nrf2-dependent Nrf2-dependent Nrf2-dependent Supraspinal

No. inflammation genes genes genes Tracts1 NM_172154 ligand dependent nuclear receptor corepressor Lcor - -3.25 - - -2 NM_010756 v-maf musculoaponeurotic fibrosarcoma Mafg - - 2.45 - -

oncogene family, protein G (avian)3 NM_008927 mitogen-activated protein kinase kinase 1 Map2k1 -2.7 -4.12 2.28 - 1.594 NM_023138 mitogen-activated protein kinase kinase 2 Map2k2 - - 2.75 1.15 1.15 NM_009157 mitogen-activated protein kinase kinase 4 Map2k4 -1.55 -3.31 - -1.68 -6 NM_011840 mitogen-activated protein kinase kinase 5 Map2k5 -1.56 - -1.45 - 1.597 NM_011943 mitogen-activated protein kinase kinase 6 Map2k6 - -3.98 - - 1.198 NM_011944 mitogen-activated protein kinase kinase 7 Map2k7 -1.32 - -0.37 -0.16 -9 NM_011945 mitogen-activated protein kinase kinase kinase 1 Map3k1 - - -0.28 -0.18 1.1110 NM_011946 mitogen-activated protein kinase kinase kinase 2 Map3k2 - -5.02 - 2.25 -11 NM_011947 mitogen-activated protein kinase kinase kinase 3 Map3k3 -2.08 - - - -12 NM_011948 mitogen-activated protein kinase kinase kinase 4 Map3k4 -1.87 -3.52 -1.98 1.51 -13 NM_008580 mitogen-activated protein kinase kinase kinase 5 Map3k5 -1.71 - - -14 NM_016693 mitogen-activated protein kinase kinase kinase 6 Map3k6 1.58 -5.25 3.25 - -15 NM_172688 mitogen-activated protein kinase kinase kinase 7 Map3k7 1.51 -3.07 2.52 3.29 -16 NM_025609 mitogen-activated protein kinase kinase kinase 7 Map3k7ip1 - -3.93 - -1.91 -

interacting protein 117 NM_138667 mitogen-activated protein kinase kinase kinase 7 Map3k7ip2 -1.56 - - - -

interacting protein 218 NM_007746 mitogen-activated protein kinase kinase kinase 8 Map3k8 -2.04 - -0.58 2.76 1.5519 NM_177395 mitogen-activated protein kinase kinase kinase 9 Map3k9 - -6.48 3.06 4.77 -20 NM_009582 mitogen-activated protein kinase kinase kinase 12 Map3k12 -7.07 -3.51 - - -21 NM_016896 mitogen-activated protein kinase kinase kinase 14 Map3k14 1.38 - 2.12 1.51 -22 NM_008279 mitogen-activated protein kinase kinase kinase kinase 1 Map4k1 - -3.35 - - 1.423 NM_009006 mitogen-activated protein kinase kinase kinase kinase 2 Map4k2 -6.72 2.35 -1.35 -24 NM_001081357 mitogen-activated protein kinase kinase kinase kinase 3 Map4k3 -1.73 - - -25 NM_008696 mitogen-activated protein kinase kinase kinase kinase 4 Map4k4 -1.58 -3.54 2.46 - -0.7426 NM_201519 mitogen-activated protein kinase kinase kinase kinase 5 Map4k5 -2.45 -3.28 3.94 -2.52 -27 NM_031248 mitogen-activated protein binding protein Mapbpip 2.29 - - -

interacting protein28 NM_001038663 mitogen-activated protein kinase 1 Mapk1 - - - 1.08 1.5129 NM_011952 mitogen-activated protein kinase 3 Mapk3 (ERK1) -1.4 -7.48 - - 1.0930 NM_172632 mitogen-activated protein kinase 4 Mapk4 - -3.51 - - 1.1131 NM_015806 mitogen-activated protein kinase 6 Mapk6 - -3.53 2.11 1.76 1.0732 NM_011841 mitogen-activated protein kinase 7 Mapk7 -16.44 - - - -0.9933 NM_016700 mitogen-activated protein kinase 8 Mapk8 - - 10.39 12.43 1.2134 NM_011162 mitogen-activated protein kinase 8 interacting protein 1 Mapk8ip1 2.22 -10.62 - - -0.8435 NM_016961 mitogen-activated protein kinase 9 Mapk9 -1.88 -4.77 1.68 2.33 1.436 NM_009158 mitogen-activated protein kinase 10 Mapk10 1.47 - 2.25 - 1.2937 NM_011161 mitogen-activated protein kinase 11 Mapk11 - -10.9 1.78 2.32 -38 NM_013871 mitogen-activated protein kinase 12 Mapk12 - - -0.24 - -0.8839 NM_011950 mitogen-activated protein kinase 13 Mapk13 1.44 - - -0.21 -40 NM_011951 mitogen-activated protein kinase 14 Mapk14 -1.62 - 1.14 -0.46 1.3841 NM_145527 mitogen-activated protein kinase activating death domain Mapkadd(Madd) - - - - 1.0342 NM_177345 mitogen-activated protein kinase associated protein 1 Mapkap1 -1.62 -6.56 -0.34 - -43 NM_008551 mitogen-activated protein kinase-activated protein kinase 2 Mapkapk2 - - - 4.31 1.3444 NM_178907 mitogen-activated protein kinase-activated protein kinase 3 Mapkapk3 - -3.39 - 2.38 1.1945 NM_010765 mitogen-activated protein kinase-activated protein kinase 5 Mapkapk5 - -4.58 -0.42 -0.47 -46 NM_011941 mitogen-activated protein kinase binding protein 1 Mapkbp1 1.42 - - - -47 NM_010881 nuclear receptor coactivator 1 Ncoa1 - -3.55 - - -48 NM_008679 nuclear receptor coactivator 3 Ncoa3 - - -0.12 - -49 NM_144892 nuclear receptor coactivator 5 Ncoa5 - - - 14.65 -50 NM_172495 nuclear receptor coactivator 7 Ncoa7 - -4.46 - - -51 NM_011308 nuclear receptor corepressor 1 Ncor1 - -5.37 3.39 - -52 NM_008689 nuclear factor of kappa light chain gene enhancer Nfkb1 -1.52 - - - 1.21

in B-cells 1, p10553 NM_019408 nuclear factor of kappa light polypeptide gene Nfkb2 -2.56 - - - -

enhancer in B-cells 2, p49/p10054 NM_010907 nuclear factor of kappa light chain gene Nfkbia - - - - -0.77

enhancer in B-cells inhibitor, alpha55 NM_030612 nuclear factor of kappa light polypeptide gene Nfkbiz - - - 2.67 -

enhancer in B-cells inhibitor, zeta56 NM_028024 NFKB inhibitor interacting Ras-like protein 2 Nkiras2 -1.33 - - - -0.9457 NM_010902 nuclear factor, erythroid derived 2, like 2 Nrf2 1.54 - - - 1.2658 NM_173440 nuclear receptor interacting protein 1 Nrip1 - - 2.63 2.87 -59 NM_020005 P300/CBP-associated factor P/caf - - - 2.33 -

Table 6.4. Canonical first-generation regulatory network members representing

putative crosstalk between Nrf2 (Nfe2l2) and Nfkb1 in inflammation-associated

carcinogenesis*

*Fold change values are listed.

300

Accession Gene Gene Name FoldNo. Symbol Change

Cell AdhesionH16245 ACTN2 actinin, alpha 2 7.02NM_018836 AJAP1 adherens junction associated protein 1 -12.26NM_004932 CDH6 cadherin 6, type 2, K-cadherin (fetal kidney) -11.09AL021786 ITM2A integral membrane protein 2A 6.51AI753143 ITGBL1 Integrin, beta-like 1 (with EGF-like repeat domains) -6.35NM_000425 L1CAM L1 cell adhesion molecule 10.84AL834134 PCDH15 protocadherin 15 9.61AF152481 PCDHA3 protocadherin alpha 3 -12.64NM_014428 TJP3 tight junction protein 3 (zona occludens 3) -11.64

Cell Division and Cell CycleNM_018123 ASPM asp (abnormal spindle) homolog, microcephaly associated (Drosophila) 9.45AL832227 BCL8 B-cell CLL/lymphoma 8 12.08AF043294 BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) 8.09AL524035 CDC2 cell division cycle 2, G1 to S and G2 to M 35.49NM_031299 CDCA3 cell division cycle associated 3 20.34NM_022112 P53AIP1 p53-regulated apoptosis-inducing protein 1 -9.86AL031680 PARD6B par-6 partitioning defective 6 homolog beta (C. elegans) 7.68

Enzymatic activityAL031848 ACOT7 acyl-CoA thioesterase 7 8.37BF224349 ASNS Asparagine synthetase 7.49BC033179 ATP8B3 ATPase, Class I, type 8B, member 3 -8.73AW294145 CMBL carboxymethylenebutenolidase homolog (Pseudomonas) 9.97AW184014 CPA5 carboxypeptidase A5 -8.09AW292273 CARS cysteinyl-tRNA synthetase -13.22BE855963 CARS2 cysteinyl-tRNA synthetase 2, mitochondrial (putative) 17.36AF110329 GLS2 glutaminase 2 (liver, mitochondrial) -11.36AI990469 HEXDC Hexosaminidase (glycosyl hydrolase family 20, catalytic domain) containing 8.71AF149304 HYALP1 hyaluronoglucosaminidase pseudogene 1 12.06AI985835 PCCB Propionyl Coenzyme A carboxylase, beta polypeptide 5.82NM_003122 SPINK1 serine peptidase inhibitor, Kazal type 1 48.45NM_001041 SI sucrase-isomaltase (alpha-glucosidase) 6.67AV691323 UGT1A1 UDP glucuronosyltransferase 1 family, polypeptide A1 11.62NM_021139 UGT2B4 UDP glucuronosyltransferase 2 family, polypeptide B4 42.39BF346193 GALNT13 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 13 ( 7.76

Extracellular RegionNM_000490 AVP arginine vasopressin (neurophysin II, antidiuretic hormone, diabetes insipidus, neurohypo 8.83NM_006419 CXCL13 chemokine (C-X-C motif) ligand 13 (B-cell chemoattractant) 27.14NM_000130 F5 coagulation factor V (proaccelerin, labile factor) 5.91NM_001131 CRISP1 cysteine-rich secretory protein 1 -15.29AW131561 DLL1 delta-like 1 (Drosophila) -7.42NM_001971 ELA2A elastase 2A -7.53NM_003665 FCN3 ficolin (collagen/fibrinogen domain containing) 3 (Hakata antigen) -10.02NM_000583 GC group-specific component (vitamin D binding protein) 15.06AF105378 HS3ST4 heparan sulfate (glucosamine) 3-O-sulfotransferase 4 8.4NM_024013 IFNA1 interferon, alpha 1 14.27AF152099 IL17C interleukin 17C -17.3AF147790 MUC12 mucin 12, cell surface associated -6.15AL022718 ODZ1 odz, odd Oz/ten-m homolog 1(Drosophila) 9.1NM_000312 PROC protein C (inactivator of coagulation factors Va and VIIIa) 79.94BC005956 RLN1 relaxin 1 9.33NM_006846 SPINK5 serine peptidase inhibitor, Kazal type 5 -10.12AI056852 SHBG sex hormone-binding globulin 16.9NM_003381 VIP vasoactive intestinal peptide 24.3

Kinases and PhosphatasesAA400121 AKAP14 A kinase (PRKA) anchor protein 14 13.38BC022297 DGKE diacylglycerol kinase, epsilon 64kDa 36.07AA971768 KSR1 kinase suppressor of ras 1 -15.18NM_000431 MVK mevalonate kinase (mevalonic aciduria) -9.6BC040177 PPM1H Protein phosphatase 1H (PP2C domain containing) -8.44AV649337 PTPN2 Protein tyrosine phosphatase, non-receptor type 2 9.31AK023365 PPFIA4 protein tyrosine phosphatase, receptor type, f polypeptide (PTPRF), interacting protein (li 10.7NM_003215 TEC tec protein tyrosine kinase -8.04

Table 7.1. Major gene clusters modulated in human prostate

cancer…..continued…

301


Proteinaceous extracellular matrixX16468 COL2A1 collagen, type II, alpha 1 (primary osteoarthritis, spondyloepiphyseal dysplasia, congenita -9.15NM_015719 COL5A3 collagen, type V, alpha 3 -11.97J04177 COL11A1 collagen, type XI, alpha 1 6.98NM_024302 MMP28 matrix metallopeptidase 28 -7.06NM_003019 SFTPD surfactant, pulmonary-associated protein D -9.13

Phosphate transportAK022765 AMACR alpha-methylacyl-CoA racemase 6.12AF061191 EDA ectodysplasin A -8.04NM_002445 MSR1 macrophage scavenger receptor 1 12.54

Regulation of transcriptionNM_020328 ACVR1B activin A receptor, type IB 10.45AF060154 MSC musculin (activated B-cell factor-1) -14.48AU159942 TOP2A topoisomerase (DNA) II alpha 170kDa 19.24BE542323 VGLL1 vestigial like 1 (Drosophila) -16.81

Transcription factor activityAK055536 ASB10 ankyrin repeat and SOCS box-containing 10 -9.17NM_021073 BMP5 bone morphogenetic protein 5 12.55NM_000623 BDKRB2 bradykinin receptor B2 -9.89AF054818 CD84 CD84 molecule -17.74AY072911 CXADR coxsackie virus and adenovirus receptor 24.8BC011877 DOCK5 dedicator of cytokinesis 5 -23.33NM_004405 DLX2 distal-less homeobox 2 14.36AF115403 ELF5 E74-like factor 5 (ets domain transcription factor) 7.35NM_173503 EFCAB3 EF-hand calcium binding domain 3 -8.68AW196403 EMX1 empty spiracles homeobox 1 15.27AF060555 ESR2 estrogen receptor 2 (ER beta) 9.12BC017869 FBXO36 F-box protein 36 17.88AF204674 FBXO40 F-box protein 40 -9.08AL119322 FGF12 fibroblast growth factor 12 -6.08AA022949 FGF18 Fibroblast growth factor 18 -20.06AI867445 FOXD3 Forkhead box D3 8.1NM_153839 GPR111 G protein-coupled receptor 111 12.05AF317652 GPR61 G protein-coupled receptor 61 16.8AL567302 GRIA1 glutamate receptor, ionotropic, AMPA 1 7.06AJ301610 GRIK2 glutamate receptor, ionotropic, kainate 2 7.09AF332227 HSFY1 heat shock transcription factor, Y-linked 1 12.07AL157773 KLHL31 kelch-like 31 (Drosophila) 8.44AI089312 LASS5 LAG1 homolog, ceramide synthase 5 12.79NM_006840 LILRB5 leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), memb 23.12AF211968 LENG3 leukocyte receptor cluster (LRC) member 3 -6.38X55503 MT4 metallothionein 4 7.6BG252318 MBD1 methyl-CpG binding domain protein 1 -7.48M96980 MYT1 myelin transcription factor 1 18.89AF192523 NPC1L1 NPC1 (Niemann-Pick disease, type C1, gene)-like 1 12.57AI051958 NR1H4 nuclear receptor subfamily 1, group H, member 4 9.94AF061056 NR1I2 nuclear receptor subfamily 1, group I, member 2 8.62BE674245 NUDT3 Nudix (nucleoside diphosphate linked moiety X)-type motif 3 9.42X89666 OR2M4 olfactory receptor, family 2, subfamily M, member 4 7.56AU145336 ONECUT2 one cut homeobox 2 13.71NM_002699 POU3F1 POU class 3 homeobox 1 8.47AF072826 RREB1 ras responsive element binding protein 1 10.35AF464736 RGS12 regulator of G-protein signaling 12 7.19NM_002924 RGS7 regulator of G-protein signaling 7 6.19NM_173560 RFXDC1 regulatory factor X domain containing 1 6.09AW469546 RUNX2 runt-related transcription factor 2 19.57AY027526 SPATA9 spermatogenesis associated 9 -11.24BF058505 SHB Src homology 2 domain containing adaptor protein B -8.31BF527050 SOX8 SRY (sex determining region Y)-box 8 -13.33NM_138780 SYTL5 synaptotagmin-like 5 -9.03NM_016170 TLX2 T-cell leukemia homeobox 2 9.07AF519569 TCTE3 t-complex-associated-testis-expressed 3 6.93AI436409 THAP11 THAP domain containing 11 -19.37AI769310 TLL1 tolloid-like 1 10.13NM_003279 TNNC2 troponin C type 2 (fast) -22.55AW873556 WNT10A Wingless-type MMTV integration site family, member 10A -11.55AY009402 WNT8A wingless-type MMTV integration site family, member 8A 7.05


302


TransportersU15688 ATP2B2 ATPase, Ca++ transporting, plasma membrane 2 -10.72AA877910 ATP2A3 ATPase, Ca++ transporting, ubiquitous -9.95NM_005177 ATP6V0A1 ATPase, H+ transporting, lysosomal V0 subunit a1 -9.01AI928218 ATP1B3 ATPase, Na+/K+ transporting, beta 3 polypeptide 9.64R44603 KCNJ9 potassium inwardly-rectifying channel, subfamily J, member 9 -9.51NM_000341 SLC3A1 solute carrier family 3 (cystine, dibasic and neutral amino acid transporters, activator of cy 8.38AB022847 SLC6A2 solute carrier family 6 (neurotransmitter transporter, noradrenalin), member 2 -14.89U16120 SLC6A6 solute carrier family 6 (neurotransmitter transporter, taurine), member 6 9.17AA708152 TMED6 transmembrane emp24 protein transport domain containing 6 23.2

MiscellaneousN93313 ATXN7L2 ataxin 7-like 2 -12.22NM_152336 AGBL1 ATP/GTP binding protein-like 1 -7.99BE551319 BTNL9 Butyrophilin-like 9 14.24AV746580 COG1 component of oligomeric golgi complex 1 8.53NM_005210 CRYGB crystallin, gamma B 12.65AI365263 DPPA5 developmental pluripotency associated 5 14.01NM_014419 DKKL1 dickkopf-like 1 (soggy) 9.9BM681417 DGCR10 DiGeorge syndrome critical region gene 10 -8.33BC020878 DNMBP dynamin binding protein 10.04AI927458 ESPN espin -8.02BF792773 FIBCD1 fibrinogen C domain containing 1 -17.19AA129444 FSTL5 follistatin-like 5 -9.33AF039196 HR hairless homolog (mouse) -8.94BQ025558 HEMK1 HemK methyltransferase family member 1 -19.63NM_003545 HIST1H4E histone cluster 1, H4e 6.25NM_002196 INSM1 insulinoma-associated 1 39.14AL022575 ITIH5L inter-alpha (globulin) inhibitor H5-like 17.92AL512748 KATNAL2 katanin p60 subunit A-like 2 -10.09AL022068 KRT18P38 keratin 18 pseudogene 38 -9.7AJ406929 KRTAP2-2 keratin associated protein 2-2 9.04BC001291 LY6K lymphocyte antigen 6 complex, locus K 8.18NM_004997 MYBPH myosin binding protein H -14.37AI709055 MYH14 myosin, heavy chain 14 7.49NM_000257 MYH6 myosin, heavy chain 6, cardiac muscle, alpha (cardiomyopathy, hypertrophic 1) 12.85AI160292 MYH7B myosin, heavy chain 7B, cardiac muscle, beta -10.96R78299 NEB Nebulin -12.71AW085558 HNT neurotrimin 7.66AK091796 NUDT22 Nudix (nucleoside diphosphate linked moiety X)-type motif 22 10.13AI925361 KCTD15 potassium channel tetramerisation domain containing 15 -14.39AI867408 Rgr Ral-GDS related protein Rgr 12.1BC014155 RHEBL1 Ras homolog enriched in brain like 1 -8.92NM_022448 RGNEF Rho-guanine nucleotide exchange factor -6.67NM_018957 SH3BP1 SH3-domain binding protein 1 9.39AI263819 SRGAP2 SLIT-ROBO Rho GTPase activating protein 2 -6.39BF062187 SNIP SNAP25-interacting protein 11.06BF435122 SYNJ1 Synaptojanin 1 -19.42AE000659 TRAalpha T cell receptor alpha locus -7.06NM_018393 TCP11L1 t-complex 11 (mouse)-like 1 -9.26NM_012457 TIMM13 translocase of inner mitochondrial membrane 13 homolog (yeast) -7.39AW969675 TRDN triadin -7.58AA868267 TRIM54 tripartite motif-containing 54 -6.8NM_003322 TULP1 tubby like protein 1 -10.32BM726860 TUBA1B tubulin, alpha 1b 7.16NM_018943 TUBA8 tubulin, alpha 8 -14.75AI122699 VASH1 Vasohibin 1 -17.66NM_080827 WFDC6 WAP four-disulfide core domain 6 14.53BF355863 ZBTB40 zinc finger and BTB domain containing 40 8.97AW469591 ZSCAN5 zinc finger and SCAN domain containing 5 -7.29

Genes upregulated or downregulated at least five-fold in human prostate cancer as

compared to normal prostate were identified by microarray analyses and are listed.

Table 7.1.

303

Table 8.1. Human prostate cell lines used in this study and their descriptors

304

Table 8.2. Genes modulated in all prostate cancer cell lines…..continued…

305


306


307


308


309


310

Genes modulated in specific prostate cancer cell lines as indicated. Fold change values

as compared to expression in normal prostate epithelial cells are listed.

Table 8.2.

311

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CURRICULUM VITA

Sujit Sukumar Nair

EDUCATION 2008 Ph.D. in Pharmaceutical Science, Rutgers, The State University of New Jersey 2001 M. Pharm. Sci. (Master of Pharmaceutical Sciences), Principal K.M. Kundnani College of Pharmacy, University of Mumbai, India 1998 B. Pharm. Sci. (Bachelor of Pharmaceutical Sciences), Bharati Vidyapeeth’s College of Pharmacy, University of Mumbai, India PROFESSIONAL EXPERIENCE 2006– 2008 Graduate Assistant, Department of Pharmaceutics, Ernest Mario School

of Pharmacy at Rutgers, The State University of New Jersey 2005– 2006 Head-Teaching Assistant, Department of Pharmaceutics, Ernest Mario

School of Pharmacy at Rutgers, The State University of New Jersey 2004 – 2005 Teaching Assistant to Dean John L. Colaizzi, Department of Pharmacy

Practice and Administration, Ernest Mario School of Pharmacy at Rutgers, The State University of New Jersey

2002 – 2004 Graduate Assistant, Department of Pharmaceutics, Ernest Mario School

of Pharmacy at Rutgers, The State University of New Jersey 1998 – 2001 Research Associate in Industrial Pharmaceutics, Principal K.M. Kundnani College of Pharmacy, University of Mumbai, India PUBLICATIONS 1] Nair, S., Li, W., Kong, A.-N.T. Natural dietary anti-cancer chemopreventive compounds: Redox-mediated differential signaling mechanisms in cytoprotection of normal cells versus cytotoxicity in tumor cells. Acta Pharmacol Sin. 2007 Apr;28(4):459-72, Invited Review. 2] Nair, S., Hebbar V., Shen, G., Gopalakrishnan, A., Khor, TO., Yu, S., Xu, C., Kong, AN. Synergistic Effects of a Combination of Dietary Factors Sulforaphane and (-)

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333

Epigallocatechin-3-gallate in HT-29 AP-1 Human Colon Carcinoma Cells. Pharm Res. 2007 Jul 27; [Epub ahead of print]. 3] Nair, S., Xu, C., Shen, G., Hebbar, V., Gopalakrishnan, A., Hu, R., Jain, MR., Liew, C., Chan, JY., Kong A.-N. T. Toxicogenomics of endoplasmic reticulum stress inducer tunicamycin in the small intestine and liver of Nrf2 knockout and C57BL/6J Mice. Toxicol Lett. 2007 Jan 10;168(1):21-39. 4] Nair, S., Xu, C., Shen, G., Hebbar, V., Gopalakrishnan, A., Hu, R., Jain, MR., Lin, W., Keum YS., Liew, C., Chan, JY., Kong A.-N. T. Pharmacogenomics of phenolic antioxidant butylated hydroxyanisole (BHA) in the small intestine and liver of Nrf2 knockout and C57BL/6J Mice. Pharm Res. 2006 Nov. 23(11):2621-37. 5] Nair, S., Barve, A., Khor, TO., Shen, G., Lin, W., Chan, JY., Cai, L., Kong AN. Regulation of gene expression by a combination of dietary factors sulforaphane and (-) epigallocatechin-3-gallate in PC-3 AP-1 human prostate adenocarcinoma cells and Nrf2-deficient murine prostate. Manuscript under consideration for publication. 6] Nair, S., Doh ST., Chan, JY., Kong AN., Cai, L. Regulatory potential for concerted modulation of Nrf2- and Nfkb1-mediated gene expression in inflammation and carcinogenesis. Manuscript under consideration for publication. 7] Nair, S., Liew, C., Khor, TO., Cai, L., Kong AN. Pharmacogenomic investigation of potential prognostic biomarkers in human prostate cancer. Manuscript under consideration for publication. 8] Nair, S., Liew, C., Khor, TO., Cai, L., Kong AN. Differential regulatory networks elicited in androgen-dependent, androgen- and estrogen-dependent and androgen-independent human prostate cancer cell lines. Manuscript under consideration for publication. 9] Shen, G., Hebbar, V., Nair, S., Xu, C., Li, W., Lin, W., Keum, YS., Han, J., Gallo, MA., Kong, A.-N. Regulation of Nrf2 transactivation domain activity. The differential effects of mitogen-activated protein kinase cascades and synergistic stimulatory effect of Raf and CREB-binding protein. J. Biol Chem 2004 May 28; 279(22):23052-60. 10] Barve, A., Khor, TO., Nair, S., Kong, AN. Pharmacogenomic profile of Novasoy®-soy isoflavone concentrate in the prostate of Nrf2 deficient and wild type mice. Journal of Pharmaceutical Sciences,. 2008 Jan 30; [Epub ahead of print]. 11] Gopalakrishnan, A., Xu, C., Nair, S., Chen, C., Hebbar, V., Kong, A.-N.T. Modulation of activator protein-1 (AP-1) and MAPK pathway by flavonoids in human prostate cancer PC3 cells. Arch Pharm Res 2006 , 29(8): 633-4. 12] Prawan, A., Keum, YS., Khor, TO., Yu, S., Nair, S., Li, W., Hu, L., Kong, AN. Structural Influence of Isothiocyanates on the Antioxidant Response Element (ARE)-


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Mediated Heme Oxygenase-1 (HO-1) Expression. Pharm Res. 2007 Jul 27; [Epub ahead of print]. 13] Shen, G., Xu, C., Hu, R., Jain, M.R., Nair, S., Lin, W., Yang, C.S., Chan, J.Y., Kong, A.-N. Comparison of (-)-epigallocatechin-3-gallate elicited liver and small intestine gene expression profiles between C57BL/6J mice and C57BL/6J/Nrf2 (-/-) mice. Pharm Res. 2005 Nov;22(11):1805-20. 14] Shen, G., Khor, TO., Hu, R., Yu, S., Nair, S., Ho, CT., Reddy, BS., Huang, MT., Newmark, HL., Kong, AN. Chemoprevention of familial adenomatous polyposis by natural dietary compounds sulforaphane and dibenzoylmethane alone and in combination in ApcMin/+ mouse. Cancer Res. 2007 Oct 15;67(20):9937-44. 15] Kim, B.R., Hu, R., Keum, Y.S., Hebbar, V., Shen, G., Nair, S., Kong, A.-N. T. Effects of Glutathione on Antioxidant Response Element-mediated Gene Expression and Apoptosis Elicited by Sulforaphane. Cancer Res. 2003 Nov 1; 63(21): 7520-5 16] Hu, R., Shen, G., Yerramilli, UR., Lin, W., Xu, C., Nair, S., Kong, A.-N.T. In vivo pharmacokinetics, activation of MAPK signaling and induction of phase II/III drug metabolizing enzymes/transporters by cancer chemopreventive compound BHA in the mice. Arch Pharm Res 2006 Oct 29 (10):9111-20. 17] Shen, G., Xu, C., Hu, R., Jain, M.R., Gopalkrishnan, A., Nair, S., Huang, M.T., Chan, J.Y., Kong A.-N. Modulation of nuclear factor E2-related factor 2-mediated gene expression in mice liver and small intestine by cancer chemopreventive agent curcumin. Mol Cancer Ther. 2006 Jan;5(1):39-51. 18] Chen, C., Pung, D., Leong, V., Hebbar, V., Shen, G., Nair, S., Li, W., Kong, A.-N. T. Induction of detoxifying enzymes by garlic organosulfur compounds through transcription factor Nrf2: effect of chemical structure and stress signals. Free Radic Biol Med. 2004 Nov 15; 37(10):1578-90. 19] Khor, T.O., Hu, R., Shen, G., Jeong, WS, Hebbar, V., Chen, C., Xu, C., Nair, S., Reddy, B., Chada, K., Kong, A.-N. T. Pharmacogenomics of cancer chemopreventive isothiocyanate compound sulforaphane in the intestinal polyps of ApcMin/+ mice. Biopharm Drug Dispos.2006, 27(9): 407-20. 20] Xu, C., Shen, G., Yuan, X., Kim , JH, Gopalakrishnan, A., Keum, YS., Nair, S., Kong, A.-N. T. ERK and JNK signaling pathways are involved in the regulation of activator protein-1 and cell death elicited by three isothiocyanates in human prostate cancer PC-3 cells. Carcinogenesis 2006 27 (3): 437-45.

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2008 SUJIT NAIR ALL RIGHTS RESERVED

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